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"source": [
"# Clustering\n",
"## 10/17/2023\n",
"print view"
]
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"source": [
"What is clustering?\n",
"\n",
"Wikipedia: Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).\n",
"\n",
"Generally speaking, clustering is NP-hard, so it is difficult to identify a provable optimal clustering.\n",
"\n",
"
\n",
"\n"
]
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"metadata": {
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"slide_type": "slide"
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"source": [
"# What is _similar_?\n",
"\n",
"* *similarity*: larger number means _more_ similar\n",
" * Tanimoto (or Jaccard) similarity of two sets:\n",
" $$\\frac{|A \\cap B|}{|A \\cup B|}$$\n",
"* *distance*: larger number means _less_ similar (zero means identical)\n",
" * Euclidean distance (L2 norm) between $n$-dimensional vectors $u$ and $v$:\n",
" $$\\|u - v\\|_2 = \\sqrt{\\sum_i^n (u_i - v_i)^2}$$\n",
" * Cityblock (Manhattan) distance (L1 norm):\n",
" $$\\|u - v\\|_1 = \\sum_i^n|u_i - v_i|$$\n",
" * Cosine distance between $n$-dimensional vectors $u$ and $v$:\n",
" $$1 - \\frac{u \\cdot v}{\\|u\\|_2 \\|v\\|_2}$$"
]
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"Help on module scipy.spatial.distance in scipy.spatial:\n",
"\n",
"NAME\n",
" scipy.spatial.distance\n",
"\n",
"DESCRIPTION\n",
" Distance computations (:mod:`scipy.spatial.distance`)\n",
" =====================================================\n",
" \n",
" .. sectionauthor:: Damian Eads\n",
" \n",
" Function reference\n",
" ------------------\n",
" \n",
" Distance matrix computation from a collection of raw observation vectors\n",
" stored in a rectangular array.\n",
" \n",
" .. autosummary::\n",
" :toctree: generated/\n",
" \n",
" pdist -- pairwise distances between observation vectors.\n",
" cdist -- distances between two collections of observation vectors\n",
" squareform -- convert distance matrix to a condensed one and vice versa\n",
" directed_hausdorff -- directed Hausdorff distance between arrays\n",
" \n",
" Predicates for checking the validity of distance matrices, both\n",
" condensed and redundant. Also contained in this module are functions\n",
" for computing the number of observations in a distance matrix.\n",
" \n",
" .. autosummary::\n",
" :toctree: generated/\n",
" \n",
" is_valid_dm -- checks for a valid distance matrix\n",
" is_valid_y -- checks for a valid condensed distance matrix\n",
" num_obs_dm -- # of observations in a distance matrix\n",
" num_obs_y -- # of observations in a condensed distance matrix\n",
" \n",
" Distance functions between two numeric vectors ``u`` and ``v``. Computing\n",
" distances over a large collection of vectors is inefficient for these\n",
" functions. Use ``pdist`` for this purpose.\n",
" \n",
" .. autosummary::\n",
" :toctree: generated/\n",
" \n",
" braycurtis -- the Bray-Curtis distance.\n",
" canberra -- the Canberra distance.\n",
" chebyshev -- the Chebyshev distance.\n",
" cityblock -- the Manhattan distance.\n",
" correlation -- the Correlation distance.\n",
" cosine -- the Cosine distance.\n",
" euclidean -- the Euclidean distance.\n",
" jensenshannon -- the Jensen-Shannon distance.\n",
" mahalanobis -- the Mahalanobis distance.\n",
" minkowski -- the Minkowski distance.\n",
" seuclidean -- the normalized Euclidean distance.\n",
" sqeuclidean -- the squared Euclidean distance.\n",
" \n",
" Distance functions between two boolean vectors (representing sets) ``u`` and\n",
" ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for\n",
" computing the distances between all pairs.\n",
" \n",
" .. autosummary::\n",
" :toctree: generated/\n",
" \n",
" dice -- the Dice dissimilarity.\n",
" hamming -- the Hamming distance.\n",
" jaccard -- the Jaccard distance.\n",
" kulczynski1 -- the Kulczynski 1 distance.\n",
" rogerstanimoto -- the Rogers-Tanimoto dissimilarity.\n",
" russellrao -- the Russell-Rao dissimilarity.\n",
" sokalmichener -- the Sokal-Michener dissimilarity.\n",
" sokalsneath -- the Sokal-Sneath dissimilarity.\n",
" yule -- the Yule dissimilarity.\n",
" \n",
" :func:`hamming` also operates over discrete numerical vectors.\n",
"\n",
"FUNCTIONS\n",
" braycurtis(u, v, w=None)\n",
" Compute the Bray-Curtis distance between two 1-D arrays.\n",
" \n",
" Bray-Curtis distance is defined as\n",
" \n",
" .. math::\n",
" \n",
" \\sum{|u_i-v_i|} / \\sum{|u_i+v_i|}\n",
" \n",
" The Bray-Curtis distance is in the range [0, 1] if all coordinates are\n",
" positive, and is undefined if the inputs are of length zero.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input array.\n",
" v : (N,) array_like\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" braycurtis : double\n",
" The Bray-Curtis distance between 1-D arrays `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.braycurtis([1, 0, 0], [0, 1, 0])\n",
" 1.0\n",
" >>> distance.braycurtis([1, 1, 0], [0, 1, 0])\n",
" 0.33333333333333331\n",
" \n",
" canberra(u, v, w=None)\n",
" Compute the Canberra distance between two 1-D arrays.\n",
" \n",
" The Canberra distance is defined as\n",
" \n",
" .. math::\n",
" \n",
" d(u,v) = \\sum_i \\frac{|u_i-v_i|}\n",
" {|u_i|+|v_i|}.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input array.\n",
" v : (N,) array_like\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" canberra : double\n",
" The Canberra distance between vectors `u` and `v`.\n",
" \n",
" Notes\n",
" -----\n",
" When `u[i]` and `v[i]` are 0 for given i, then the fraction 0/0 = 0 is\n",
" used in the calculation.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.canberra([1, 0, 0], [0, 1, 0])\n",
" 2.0\n",
" >>> distance.canberra([1, 1, 0], [0, 1, 0])\n",
" 1.0\n",
" \n",
" cdist(XA, XB, metric='euclidean', *, out=None, **kwargs)\n",
" Compute distance between each pair of the two collections of inputs.\n",
" \n",
" See Notes for common calling conventions.\n",
" \n",
" Parameters\n",
" ----------\n",
" XA : array_like\n",
" An :math:`m_A` by :math:`n` array of :math:`m_A`\n",
" original observations in an :math:`n`-dimensional space.\n",
" Inputs are converted to float type.\n",
" XB : array_like\n",
" An :math:`m_B` by :math:`n` array of :math:`m_B`\n",
" original observations in an :math:`n`-dimensional space.\n",
" Inputs are converted to float type.\n",
" metric : str or callable, optional\n",
" The distance metric to use. If a string, the distance function can be\n",
" 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation',\n",
" 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'jensenshannon',\n",
" 'kulczynski1', 'mahalanobis', 'matching', 'minkowski',\n",
" 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener',\n",
" 'sokalsneath', 'sqeuclidean', 'yule'.\n",
" **kwargs : dict, optional\n",
" Extra arguments to `metric`: refer to each metric documentation for a\n",
" list of all possible arguments.\n",
" \n",
" Some possible arguments:\n",
" \n",
" p : scalar\n",
" The p-norm to apply for Minkowski, weighted and unweighted.\n",
" Default: 2.\n",
" \n",
" w : array_like\n",
" The weight vector for metrics that support weights (e.g., Minkowski).\n",
" \n",
" V : array_like\n",
" The variance vector for standardized Euclidean.\n",
" Default: var(vstack([XA, XB]), axis=0, ddof=1)\n",
" \n",
" VI : array_like\n",
" The inverse of the covariance matrix for Mahalanobis.\n",
" Default: inv(cov(vstack([XA, XB].T))).T\n",
" \n",
" out : ndarray\n",
" The output array\n",
" If not None, the distance matrix Y is stored in this array.\n",
" \n",
" Returns\n",
" -------\n",
" Y : ndarray\n",
" A :math:`m_A` by :math:`m_B` distance matrix is returned.\n",
" For each :math:`i` and :math:`j`, the metric\n",
" ``dist(u=XA[i], v=XB[j])`` is computed and stored in the\n",
" :math:`ij` th entry.\n",
" \n",
" Raises\n",
" ------\n",
" ValueError\n",
" An exception is thrown if `XA` and `XB` do not have\n",
" the same number of columns.\n",
" \n",
" Notes\n",
" -----\n",
" The following are common calling conventions:\n",
" \n",
" 1. ``Y = cdist(XA, XB, 'euclidean')``\n",
" \n",
" Computes the distance between :math:`m` points using\n",
" Euclidean distance (2-norm) as the distance metric between the\n",
" points. The points are arranged as :math:`m`\n",
" :math:`n`-dimensional row vectors in the matrix X.\n",
" \n",
" 2. ``Y = cdist(XA, XB, 'minkowski', p=2.)``\n",
" \n",
" Computes the distances using the Minkowski distance\n",
" :math:`\\|u-v\\|_p` (:math:`p`-norm) where :math:`p > 0` (note\n",
" that this is only a quasi-metric if :math:`0 < p < 1`).\n",
" \n",
" 3. ``Y = cdist(XA, XB, 'cityblock')``\n",
" \n",
" Computes the city block or Manhattan distance between the\n",
" points.\n",
" \n",
" 4. ``Y = cdist(XA, XB, 'seuclidean', V=None)``\n",
" \n",
" Computes the standardized Euclidean distance. The standardized\n",
" Euclidean distance between two n-vectors ``u`` and ``v`` is\n",
" \n",
" .. math::\n",
" \n",
" \\sqrt{\\sum {(u_i-v_i)^2 / V[x_i]}}.\n",
" \n",
" V is the variance vector; V[i] is the variance computed over all\n",
" the i'th components of the points. If not passed, it is\n",
" automatically computed.\n",
" \n",
" 5. ``Y = cdist(XA, XB, 'sqeuclidean')``\n",
" \n",
" Computes the squared Euclidean distance :math:`\\|u-v\\|_2^2` between\n",
" the vectors.\n",
" \n",
" 6. ``Y = cdist(XA, XB, 'cosine')``\n",
" \n",
" Computes the cosine distance between vectors u and v,\n",
" \n",
" .. math::\n",
" \n",
" 1 - \\frac{u \\cdot v}\n",
" {{\\|u\\|}_2 {\\|v\\|}_2}\n",
" \n",
" where :math:`\\|*\\|_2` is the 2-norm of its argument ``*``, and\n",
" :math:`u \\cdot v` is the dot product of :math:`u` and :math:`v`.\n",
" \n",
" 7. ``Y = cdist(XA, XB, 'correlation')``\n",
" \n",
" Computes the correlation distance between vectors u and v. This is\n",
" \n",
" .. math::\n",
" \n",
" 1 - \\frac{(u - \\bar{u}) \\cdot (v - \\bar{v})}\n",
" {{\\|(u - \\bar{u})\\|}_2 {\\|(v - \\bar{v})\\|}_2}\n",
" \n",
" where :math:`\\bar{v}` is the mean of the elements of vector v,\n",
" and :math:`x \\cdot y` is the dot product of :math:`x` and :math:`y`.\n",
" \n",
" \n",
" 8. ``Y = cdist(XA, XB, 'hamming')``\n",
" \n",
" Computes the normalized Hamming distance, or the proportion of\n",
" those vector elements between two n-vectors ``u`` and ``v``\n",
" which disagree. To save memory, the matrix ``X`` can be of type\n",
" boolean.\n",
" \n",
" 9. ``Y = cdist(XA, XB, 'jaccard')``\n",
" \n",
" Computes the Jaccard distance between the points. Given two\n",
" vectors, ``u`` and ``v``, the Jaccard distance is the\n",
" proportion of those elements ``u[i]`` and ``v[i]`` that\n",
" disagree where at least one of them is non-zero.\n",
" \n",
" 10. ``Y = cdist(XA, XB, 'jensenshannon')``\n",
" \n",
" Computes the Jensen-Shannon distance between two probability arrays.\n",
" Given two probability vectors, :math:`p` and :math:`q`, the\n",
" Jensen-Shannon distance is\n",
" \n",
" .. math::\n",
" \n",
" \\sqrt{\\frac{D(p \\parallel m) + D(q \\parallel m)}{2}}\n",
" \n",
" where :math:`m` is the pointwise mean of :math:`p` and :math:`q`\n",
" and :math:`D` is the Kullback-Leibler divergence.\n",
" \n",
" 11. ``Y = cdist(XA, XB, 'chebyshev')``\n",
" \n",
" Computes the Chebyshev distance between the points. The\n",
" Chebyshev distance between two n-vectors ``u`` and ``v`` is the\n",
" maximum norm-1 distance between their respective elements. More\n",
" precisely, the distance is given by\n",
" \n",
" .. math::\n",
" \n",
" d(u,v) = \\max_i {|u_i-v_i|}.\n",
" \n",
" 12. ``Y = cdist(XA, XB, 'canberra')``\n",
" \n",
" Computes the Canberra distance between the points. The\n",
" Canberra distance between two points ``u`` and ``v`` is\n",
" \n",
" .. math::\n",
" \n",
" d(u,v) = \\sum_i \\frac{|u_i-v_i|}\n",
" {|u_i|+|v_i|}.\n",
" \n",
" 13. ``Y = cdist(XA, XB, 'braycurtis')``\n",
" \n",
" Computes the Bray-Curtis distance between the points. The\n",
" Bray-Curtis distance between two points ``u`` and ``v`` is\n",
" \n",
" \n",
" .. math::\n",
" \n",
" d(u,v) = \\frac{\\sum_i (|u_i-v_i|)}\n",
" {\\sum_i (|u_i+v_i|)}\n",
" \n",
" 14. ``Y = cdist(XA, XB, 'mahalanobis', VI=None)``\n",
" \n",
" Computes the Mahalanobis distance between the points. The\n",
" Mahalanobis distance between two points ``u`` and ``v`` is\n",
" :math:`\\sqrt{(u-v)(1/V)(u-v)^T}` where :math:`(1/V)` (the ``VI``\n",
" variable) is the inverse covariance. If ``VI`` is not None,\n",
" ``VI`` will be used as the inverse covariance matrix.\n",
" \n",
" 15. ``Y = cdist(XA, XB, 'yule')``\n",
" \n",
" Computes the Yule distance between the boolean\n",
" vectors. (see `yule` function documentation)\n",
" \n",
" 16. ``Y = cdist(XA, XB, 'matching')``\n",
" \n",
" Synonym for 'hamming'.\n",
" \n",
" 17. ``Y = cdist(XA, XB, 'dice')``\n",
" \n",
" Computes the Dice distance between the boolean vectors. (see\n",
" `dice` function documentation)\n",
" \n",
" 18. ``Y = cdist(XA, XB, 'kulczynski1')``\n",
" \n",
" Computes the kulczynski distance between the boolean\n",
" vectors. (see `kulczynski1` function documentation)\n",
" \n",
" 19. ``Y = cdist(XA, XB, 'rogerstanimoto')``\n",
" \n",
" Computes the Rogers-Tanimoto distance between the boolean\n",
" vectors. (see `rogerstanimoto` function documentation)\n",
" \n",
" 20. ``Y = cdist(XA, XB, 'russellrao')``\n",
" \n",
" Computes the Russell-Rao distance between the boolean\n",
" vectors. (see `russellrao` function documentation)\n",
" \n",
" 21. ``Y = cdist(XA, XB, 'sokalmichener')``\n",
" \n",
" Computes the Sokal-Michener distance between the boolean\n",
" vectors. (see `sokalmichener` function documentation)\n",
" \n",
" 22. ``Y = cdist(XA, XB, 'sokalsneath')``\n",
" \n",
" Computes the Sokal-Sneath distance between the vectors. (see\n",
" `sokalsneath` function documentation)\n",
" \n",
" 23. ``Y = cdist(XA, XB, f)``\n",
" \n",
" Computes the distance between all pairs of vectors in X\n",
" using the user supplied 2-arity function f. For example,\n",
" Euclidean distance between the vectors could be computed\n",
" as follows::\n",
" \n",
" dm = cdist(XA, XB, lambda u, v: np.sqrt(((u-v)**2).sum()))\n",
" \n",
" Note that you should avoid passing a reference to one of\n",
" the distance functions defined in this library. For example,::\n",
" \n",
" dm = cdist(XA, XB, sokalsneath)\n",
" \n",
" would calculate the pair-wise distances between the vectors in\n",
" X using the Python function `sokalsneath`. This would result in\n",
" sokalsneath being called :math:`{n \\choose 2}` times, which\n",
" is inefficient. Instead, the optimized C version is more\n",
" efficient, and we call it using the following syntax::\n",
" \n",
" dm = cdist(XA, XB, 'sokalsneath')\n",
" \n",
" Examples\n",
" --------\n",
" Find the Euclidean distances between four 2-D coordinates:\n",
" \n",
" >>> from scipy.spatial import distance\n",
" >>> import numpy as np\n",
" >>> coords = [(35.0456, -85.2672),\n",
" ... (35.1174, -89.9711),\n",
" ... (35.9728, -83.9422),\n",
" ... (36.1667, -86.7833)]\n",
" >>> distance.cdist(coords, coords, 'euclidean')\n",
" array([[ 0. , 4.7044, 1.6172, 1.8856],\n",
" [ 4.7044, 0. , 6.0893, 3.3561],\n",
" [ 1.6172, 6.0893, 0. , 2.8477],\n",
" [ 1.8856, 3.3561, 2.8477, 0. ]])\n",
" \n",
" \n",
" Find the Manhattan distance from a 3-D point to the corners of the unit\n",
" cube:\n",
" \n",
" >>> a = np.array([[0, 0, 0],\n",
" ... [0, 0, 1],\n",
" ... [0, 1, 0],\n",
" ... [0, 1, 1],\n",
" ... [1, 0, 0],\n",
" ... [1, 0, 1],\n",
" ... [1, 1, 0],\n",
" ... [1, 1, 1]])\n",
" >>> b = np.array([[ 0.1, 0.2, 0.4]])\n",
" >>> distance.cdist(a, b, 'cityblock')\n",
" array([[ 0.7],\n",
" [ 0.9],\n",
" [ 1.3],\n",
" [ 1.5],\n",
" [ 1.5],\n",
" [ 1.7],\n",
" [ 2.1],\n",
" [ 2.3]])\n",
" \n",
" chebyshev(u, v, w=None)\n",
" Compute the Chebyshev distance.\n",
" \n",
" Computes the Chebyshev distance between two 1-D arrays `u` and `v`,\n",
" which is defined as\n",
" \n",
" .. math::\n",
" \n",
" \\max_i {|u_i-v_i|}.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input vector.\n",
" v : (N,) array_like\n",
" Input vector.\n",
" w : (N,) array_like, optional\n",
" Unused, as 'max' is a weightless operation. Here for API consistency.\n",
" \n",
" Returns\n",
" -------\n",
" chebyshev : double\n",
" The Chebyshev distance between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.chebyshev([1, 0, 0], [0, 1, 0])\n",
" 1\n",
" >>> distance.chebyshev([1, 1, 0], [0, 1, 0])\n",
" 1\n",
" \n",
" cityblock(u, v, w=None)\n",
" Compute the City Block (Manhattan) distance.\n",
" \n",
" Computes the Manhattan distance between two 1-D arrays `u` and `v`,\n",
" which is defined as\n",
" \n",
" .. math::\n",
" \n",
" \\sum_i {\\left| u_i - v_i \\right|}.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input array.\n",
" v : (N,) array_like\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" cityblock : double\n",
" The City Block (Manhattan) distance between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.cityblock([1, 0, 0], [0, 1, 0])\n",
" 2\n",
" >>> distance.cityblock([1, 0, 0], [0, 2, 0])\n",
" 3\n",
" >>> distance.cityblock([1, 0, 0], [1, 1, 0])\n",
" 1\n",
" \n",
" correlation(u, v, w=None, centered=True)\n",
" Compute the correlation distance between two 1-D arrays.\n",
" \n",
" The correlation distance between `u` and `v`, is\n",
" defined as\n",
" \n",
" .. math::\n",
" \n",
" 1 - \\frac{(u - \\bar{u}) \\cdot (v - \\bar{v})}\n",
" {{\\|(u - \\bar{u})\\|}_2 {\\|(v - \\bar{v})\\|}_2}\n",
" \n",
" where :math:`\\bar{u}` is the mean of the elements of `u`\n",
" and :math:`x \\cdot y` is the dot product of :math:`x` and :math:`y`.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input array.\n",
" v : (N,) array_like\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" centered : bool, optional\n",
" If True, `u` and `v` will be centered. Default is True.\n",
" \n",
" Returns\n",
" -------\n",
" correlation : double\n",
" The correlation distance between 1-D array `u` and `v`.\n",
" \n",
" cosine(u, v, w=None)\n",
" Compute the Cosine distance between 1-D arrays.\n",
" \n",
" The Cosine distance between `u` and `v`, is defined as\n",
" \n",
" .. math::\n",
" \n",
" 1 - \\frac{u \\cdot v}\n",
" {\\|u\\|_2 \\|v\\|_2}.\n",
" \n",
" where :math:`u \\cdot v` is the dot product of :math:`u` and\n",
" :math:`v`.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input array.\n",
" v : (N,) array_like\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" cosine : double\n",
" The Cosine distance between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.cosine([1, 0, 0], [0, 1, 0])\n",
" 1.0\n",
" >>> distance.cosine([100, 0, 0], [0, 1, 0])\n",
" 1.0\n",
" >>> distance.cosine([1, 1, 0], [0, 1, 0])\n",
" 0.29289321881345254\n",
" \n",
" dice(u, v, w=None)\n",
" Compute the Dice dissimilarity between two boolean 1-D arrays.\n",
" \n",
" The Dice dissimilarity between `u` and `v`, is\n",
" \n",
" .. math::\n",
" \n",
" \\frac{c_{TF} + c_{FT}}\n",
" {2c_{TT} + c_{FT} + c_{TF}}\n",
" \n",
" where :math:`c_{ij}` is the number of occurrences of\n",
" :math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for\n",
" :math:`k < n`.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like, bool\n",
" Input 1-D array.\n",
" v : (N,) array_like, bool\n",
" Input 1-D array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" dice : double\n",
" The Dice dissimilarity between 1-D arrays `u` and `v`.\n",
" \n",
" Notes\n",
" -----\n",
" This function computes the Dice dissimilarity index. To compute the\n",
" Dice similarity index, convert one to the other with similarity =\n",
" 1 - dissimilarity.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.dice([1, 0, 0], [0, 1, 0])\n",
" 1.0\n",
" >>> distance.dice([1, 0, 0], [1, 1, 0])\n",
" 0.3333333333333333\n",
" >>> distance.dice([1, 0, 0], [2, 0, 0])\n",
" -0.3333333333333333\n",
" \n",
" directed_hausdorff(u, v, seed=0)\n",
" Compute the directed Hausdorff distance between two 2-D arrays.\n",
" \n",
" Distances between pairs are calculated using a Euclidean metric.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (M,N) array_like\n",
" Input array.\n",
" v : (O,N) array_like\n",
" Input array.\n",
" seed : int or None\n",
" Local `numpy.random.RandomState` seed. Default is 0, a random\n",
" shuffling of u and v that guarantees reproducibility.\n",
" \n",
" Returns\n",
" -------\n",
" d : double\n",
" The directed Hausdorff distance between arrays `u` and `v`,\n",
" \n",
" index_1 : int\n",
" index of point contributing to Hausdorff pair in `u`\n",
" \n",
" index_2 : int\n",
" index of point contributing to Hausdorff pair in `v`\n",
" \n",
" Raises\n",
" ------\n",
" ValueError\n",
" An exception is thrown if `u` and `v` do not have\n",
" the same number of columns.\n",
" \n",
" See Also\n",
" --------\n",
" scipy.spatial.procrustes : Another similarity test for two data sets\n",
" \n",
" Notes\n",
" -----\n",
" Uses the early break technique and the random sampling approach\n",
" described by [1]_. Although worst-case performance is ``O(m * o)``\n",
" (as with the brute force algorithm), this is unlikely in practice\n",
" as the input data would have to require the algorithm to explore\n",
" every single point interaction, and after the algorithm shuffles\n",
" the input points at that. The best case performance is O(m), which\n",
" is satisfied by selecting an inner loop distance that is less than\n",
" cmax and leads to an early break as often as possible. The authors\n",
" have formally shown that the average runtime is closer to O(m).\n",
" \n",
" .. versionadded:: 0.19.0\n",
" \n",
" References\n",
" ----------\n",
" .. [1] A. A. Taha and A. Hanbury, \"An efficient algorithm for\n",
" calculating the exact Hausdorff distance.\" IEEE Transactions On\n",
" Pattern Analysis And Machine Intelligence, vol. 37 pp. 2153-63,\n",
" 2015.\n",
" \n",
" Examples\n",
" --------\n",
" Find the directed Hausdorff distance between two 2-D arrays of\n",
" coordinates:\n",
" \n",
" >>> from scipy.spatial.distance import directed_hausdorff\n",
" >>> import numpy as np\n",
" >>> u = np.array([(1.0, 0.0),\n",
" ... (0.0, 1.0),\n",
" ... (-1.0, 0.0),\n",
" ... (0.0, -1.0)])\n",
" >>> v = np.array([(2.0, 0.0),\n",
" ... (0.0, 2.0),\n",
" ... (-2.0, 0.0),\n",
" ... (0.0, -4.0)])\n",
" \n",
" >>> directed_hausdorff(u, v)[0]\n",
" 2.23606797749979\n",
" >>> directed_hausdorff(v, u)[0]\n",
" 3.0\n",
" \n",
" Find the general (symmetric) Hausdorff distance between two 2-D\n",
" arrays of coordinates:\n",
" \n",
" >>> max(directed_hausdorff(u, v)[0], directed_hausdorff(v, u)[0])\n",
" 3.0\n",
" \n",
" Find the indices of the points that generate the Hausdorff distance\n",
" (the Hausdorff pair):\n",
" \n",
" >>> directed_hausdorff(v, u)[1:]\n",
" (3, 3)\n",
" \n",
" euclidean(u, v, w=None)\n",
" Computes the Euclidean distance between two 1-D arrays.\n",
" \n",
" The Euclidean distance between 1-D arrays `u` and `v`, is defined as\n",
" \n",
" .. math::\n",
" \n",
" {\\|u-v\\|}_2\n",
" \n",
" \\left(\\sum{(w_i |(u_i - v_i)|^2)}\\right)^{1/2}\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input array.\n",
" v : (N,) array_like\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" euclidean : double\n",
" The Euclidean distance between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.euclidean([1, 0, 0], [0, 1, 0])\n",
" 1.4142135623730951\n",
" >>> distance.euclidean([1, 1, 0], [0, 1, 0])\n",
" 1.0\n",
" \n",
" hamming(u, v, w=None)\n",
" Compute the Hamming distance between two 1-D arrays.\n",
" \n",
" The Hamming distance between 1-D arrays `u` and `v`, is simply the\n",
" proportion of disagreeing components in `u` and `v`. If `u` and `v` are\n",
" boolean vectors, the Hamming distance is\n",
" \n",
" .. math::\n",
" \n",
" \\frac{c_{01} + c_{10}}{n}\n",
" \n",
" where :math:`c_{ij}` is the number of occurrences of\n",
" :math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for\n",
" :math:`k < n`.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input array.\n",
" v : (N,) array_like\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" hamming : double\n",
" The Hamming distance between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.hamming([1, 0, 0], [0, 1, 0])\n",
" 0.66666666666666663\n",
" >>> distance.hamming([1, 0, 0], [1, 1, 0])\n",
" 0.33333333333333331\n",
" >>> distance.hamming([1, 0, 0], [2, 0, 0])\n",
" 0.33333333333333331\n",
" >>> distance.hamming([1, 0, 0], [3, 0, 0])\n",
" 0.33333333333333331\n",
" \n",
" is_valid_dm(D, tol=0.0, throw=False, name='D', warning=False)\n",
" Return True if input array is a valid distance matrix.\n",
" \n",
" Distance matrices must be 2-dimensional numpy arrays.\n",
" They must have a zero-diagonal, and they must be symmetric.\n",
" \n",
" Parameters\n",
" ----------\n",
" D : array_like\n",
" The candidate object to test for validity.\n",
" tol : float, optional\n",
" The distance matrix should be symmetric. `tol` is the maximum\n",
" difference between entries ``ij`` and ``ji`` for the distance\n",
" metric to be considered symmetric.\n",
" throw : bool, optional\n",
" An exception is thrown if the distance matrix passed is not valid.\n",
" name : str, optional\n",
" The name of the variable to checked. This is useful if\n",
" throw is set to True so the offending variable can be identified\n",
" in the exception message when an exception is thrown.\n",
" warning : bool, optional\n",
" Instead of throwing an exception, a warning message is\n",
" raised.\n",
" \n",
" Returns\n",
" -------\n",
" valid : bool\n",
" True if the variable `D` passed is a valid distance matrix.\n",
" \n",
" Notes\n",
" -----\n",
" Small numerical differences in `D` and `D.T` and non-zeroness of\n",
" the diagonal are ignored if they are within the tolerance specified\n",
" by `tol`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> import numpy as np\n",
" >>> from scipy.spatial.distance import is_valid_dm\n",
" \n",
" This matrix is a valid distance matrix.\n",
" \n",
" >>> d = np.array([[0.0, 1.1, 1.2, 1.3],\n",
" ... [1.1, 0.0, 1.0, 1.4],\n",
" ... [1.2, 1.0, 0.0, 1.5],\n",
" ... [1.3, 1.4, 1.5, 0.0]])\n",
" >>> is_valid_dm(d)\n",
" True\n",
" \n",
" In the following examples, the input is not a valid distance matrix.\n",
" \n",
" Not square:\n",
" \n",
" >>> is_valid_dm([[0, 2, 2], [2, 0, 2]])\n",
" False\n",
" \n",
" Nonzero diagonal element:\n",
" \n",
" >>> is_valid_dm([[0, 1, 1], [1, 2, 3], [1, 3, 0]])\n",
" False\n",
" \n",
" Not symmetric:\n",
" \n",
" >>> is_valid_dm([[0, 1, 3], [2, 0, 1], [3, 1, 0]])\n",
" False\n",
" \n",
" is_valid_y(y, warning=False, throw=False, name=None)\n",
" Return True if the input array is a valid condensed distance matrix.\n",
" \n",
" Condensed distance matrices must be 1-dimensional numpy arrays.\n",
" Their length must be a binomial coefficient :math:`{n \\choose 2}`\n",
" for some positive integer n.\n",
" \n",
" Parameters\n",
" ----------\n",
" y : array_like\n",
" The condensed distance matrix.\n",
" warning : bool, optional\n",
" Invokes a warning if the variable passed is not a valid\n",
" condensed distance matrix. The warning message explains why\n",
" the distance matrix is not valid. `name` is used when\n",
" referencing the offending variable.\n",
" throw : bool, optional\n",
" Throws an exception if the variable passed is not a valid\n",
" condensed distance matrix.\n",
" name : bool, optional\n",
" Used when referencing the offending variable in the\n",
" warning or exception message.\n",
" \n",
" Returns\n",
" -------\n",
" bool\n",
" True if the input array is a valid condensed distance matrix,\n",
" False otherwise.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial.distance import is_valid_y\n",
" \n",
" This vector is a valid condensed distance matrix. The length is 6,\n",
" which corresponds to ``n = 4``, since ``4*(4 - 1)/2`` is 6.\n",
" \n",
" >>> v = [1.0, 1.2, 1.0, 0.5, 1.3, 0.9]\n",
" >>> is_valid_y(v)\n",
" True\n",
" \n",
" An input vector with length, say, 7, is not a valid condensed distance\n",
" matrix.\n",
" \n",
" >>> is_valid_y([1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7])\n",
" False\n",
" \n",
" jaccard(u, v, w=None)\n",
" Compute the Jaccard-Needham dissimilarity between two boolean 1-D arrays.\n",
" \n",
" The Jaccard-Needham dissimilarity between 1-D boolean arrays `u` and `v`,\n",
" is defined as\n",
" \n",
" .. math::\n",
" \n",
" \\frac{c_{TF} + c_{FT}}\n",
" {c_{TT} + c_{FT} + c_{TF}}\n",
" \n",
" where :math:`c_{ij}` is the number of occurrences of\n",
" :math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for\n",
" :math:`k < n`.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like, bool\n",
" Input array.\n",
" v : (N,) array_like, bool\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" jaccard : double\n",
" The Jaccard distance between vectors `u` and `v`.\n",
" \n",
" Notes\n",
" -----\n",
" When both `u` and `v` lead to a `0/0` division i.e. there is no overlap\n",
" between the items in the vectors the returned distance is 0. See the\n",
" Wikipedia page on the Jaccard index [1]_, and this paper [2]_.\n",
" \n",
" .. versionchanged:: 1.2.0\n",
" Previously, when `u` and `v` lead to a `0/0` division, the function\n",
" would return NaN. This was changed to return 0 instead.\n",
" \n",
" References\n",
" ----------\n",
" .. [1] https://en.wikipedia.org/wiki/Jaccard_index\n",
" .. [2] S. Kosub, \"A note on the triangle inequality for the Jaccard\n",
" distance\", 2016, :arxiv:`1612.02696`\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.jaccard([1, 0, 0], [0, 1, 0])\n",
" 1.0\n",
" >>> distance.jaccard([1, 0, 0], [1, 1, 0])\n",
" 0.5\n",
" >>> distance.jaccard([1, 0, 0], [1, 2, 0])\n",
" 0.5\n",
" >>> distance.jaccard([1, 0, 0], [1, 1, 1])\n",
" 0.66666666666666663\n",
" \n",
" jensenshannon(p, q, base=None, *, axis=0, keepdims=False)\n",
" Compute the Jensen-Shannon distance (metric) between\n",
" two probability arrays. This is the square root\n",
" of the Jensen-Shannon divergence.\n",
" \n",
" The Jensen-Shannon distance between two probability\n",
" vectors `p` and `q` is defined as,\n",
" \n",
" .. math::\n",
" \n",
" \\sqrt{\\frac{D(p \\parallel m) + D(q \\parallel m)}{2}}\n",
" \n",
" where :math:`m` is the pointwise mean of :math:`p` and :math:`q`\n",
" and :math:`D` is the Kullback-Leibler divergence.\n",
" \n",
" This routine will normalize `p` and `q` if they don't sum to 1.0.\n",
" \n",
" Parameters\n",
" ----------\n",
" p : (N,) array_like\n",
" left probability vector\n",
" q : (N,) array_like\n",
" right probability vector\n",
" base : double, optional\n",
" the base of the logarithm used to compute the output\n",
" if not given, then the routine uses the default base of\n",
" scipy.stats.entropy.\n",
" axis : int, optional\n",
" Axis along which the Jensen-Shannon distances are computed. The default\n",
" is 0.\n",
" \n",
" .. versionadded:: 1.7.0\n",
" keepdims : bool, optional\n",
" If this is set to `True`, the reduced axes are left in the\n",
" result as dimensions with size one. With this option,\n",
" the result will broadcast correctly against the input array.\n",
" Default is False.\n",
" \n",
" .. versionadded:: 1.7.0\n",
" \n",
" Returns\n",
" -------\n",
" js : double or ndarray\n",
" The Jensen-Shannon distances between `p` and `q` along the `axis`.\n",
" \n",
" Notes\n",
" -----\n",
" \n",
" .. versionadded:: 1.2.0\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> import numpy as np\n",
" >>> distance.jensenshannon([1.0, 0.0, 0.0], [0.0, 1.0, 0.0], 2.0)\n",
" 1.0\n",
" >>> distance.jensenshannon([1.0, 0.0], [0.5, 0.5])\n",
" 0.46450140402245893\n",
" >>> distance.jensenshannon([1.0, 0.0, 0.0], [1.0, 0.0, 0.0])\n",
" 0.0\n",
" >>> a = np.array([[1, 2, 3, 4],\n",
" ... [5, 6, 7, 8],\n",
" ... [9, 10, 11, 12]])\n",
" >>> b = np.array([[13, 14, 15, 16],\n",
" ... [17, 18, 19, 20],\n",
" ... [21, 22, 23, 24]])\n",
" >>> distance.jensenshannon(a, b, axis=0)\n",
" array([0.1954288, 0.1447697, 0.1138377, 0.0927636])\n",
" >>> distance.jensenshannon(a, b, axis=1)\n",
" array([0.1402339, 0.0399106, 0.0201815])\n",
" \n",
" kulczynski1(u, v, *, w=None)\n",
" Compute the Kulczynski 1 dissimilarity between two boolean 1-D arrays.\n",
" \n",
" The Kulczynski 1 dissimilarity between two boolean 1-D arrays `u` and `v`\n",
" of length ``n``, is defined as\n",
" \n",
" .. math::\n",
" \n",
" \\frac{c_{11}}\n",
" {c_{01} + c_{10}}\n",
" \n",
" where :math:`c_{ij}` is the number of occurrences of\n",
" :math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for\n",
" :math:`k \\in {0, 1, ..., n-1}`.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like, bool\n",
" Input array.\n",
" v : (N,) array_like, bool\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" kulczynski1 : float\n",
" The Kulczynski 1 distance between vectors `u` and `v`.\n",
" \n",
" Notes\n",
" -----\n",
" This measure has a minimum value of 0 and no upper limit.\n",
" It is un-defined when there are no non-matches.\n",
" \n",
" .. versionadded:: 1.8.0\n",
" \n",
" References\n",
" ----------\n",
" .. [1] Kulczynski S. et al. Bulletin\n",
" International de l'Academie Polonaise des Sciences\n",
" et des Lettres, Classe des Sciences Mathematiques\n",
" et Naturelles, Serie B (Sciences Naturelles). 1927;\n",
" Supplement II: 57-203.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.kulczynski1([1, 0, 0], [0, 1, 0])\n",
" 0.0\n",
" >>> distance.kulczynski1([True, False, False], [True, True, False])\n",
" 1.0\n",
" >>> distance.kulczynski1([True, False, False], [True])\n",
" 0.5\n",
" >>> distance.kulczynski1([1, 0, 0], [3, 1, 0])\n",
" -3.0\n",
" \n",
" mahalanobis(u, v, VI)\n",
" Compute the Mahalanobis distance between two 1-D arrays.\n",
" \n",
" The Mahalanobis distance between 1-D arrays `u` and `v`, is defined as\n",
" \n",
" .. math::\n",
" \n",
" \\sqrt{ (u-v) V^{-1} (u-v)^T }\n",
" \n",
" where ``V`` is the covariance matrix. Note that the argument `VI`\n",
" is the inverse of ``V``.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input array.\n",
" v : (N,) array_like\n",
" Input array.\n",
" VI : array_like\n",
" The inverse of the covariance matrix.\n",
" \n",
" Returns\n",
" -------\n",
" mahalanobis : double\n",
" The Mahalanobis distance between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> iv = [[1, 0.5, 0.5], [0.5, 1, 0.5], [0.5, 0.5, 1]]\n",
" >>> distance.mahalanobis([1, 0, 0], [0, 1, 0], iv)\n",
" 1.0\n",
" >>> distance.mahalanobis([0, 2, 0], [0, 1, 0], iv)\n",
" 1.0\n",
" >>> distance.mahalanobis([2, 0, 0], [0, 1, 0], iv)\n",
" 1.7320508075688772\n",
" \n",
" minkowski(u, v, p=2, w=None)\n",
" Compute the Minkowski distance between two 1-D arrays.\n",
" \n",
" The Minkowski distance between 1-D arrays `u` and `v`,\n",
" is defined as\n",
" \n",
" .. math::\n",
" \n",
" {\\|u-v\\|}_p = (\\sum{|u_i - v_i|^p})^{1/p}.\n",
" \n",
" \n",
" \\left(\\sum{w_i(|(u_i - v_i)|^p)}\\right)^{1/p}.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input array.\n",
" v : (N,) array_like\n",
" Input array.\n",
" p : scalar\n",
" The order of the norm of the difference :math:`{\\|u-v\\|}_p`. Note\n",
" that for :math:`0 < p < 1`, the triangle inequality only holds with\n",
" an additional multiplicative factor, i.e. it is only a quasi-metric.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" minkowski : double\n",
" The Minkowski distance between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.minkowski([1, 0, 0], [0, 1, 0], 1)\n",
" 2.0\n",
" >>> distance.minkowski([1, 0, 0], [0, 1, 0], 2)\n",
" 1.4142135623730951\n",
" >>> distance.minkowski([1, 0, 0], [0, 1, 0], 3)\n",
" 1.2599210498948732\n",
" >>> distance.minkowski([1, 1, 0], [0, 1, 0], 1)\n",
" 1.0\n",
" >>> distance.minkowski([1, 1, 0], [0, 1, 0], 2)\n",
" 1.0\n",
" >>> distance.minkowski([1, 1, 0], [0, 1, 0], 3)\n",
" 1.0\n",
" \n",
" num_obs_dm(d)\n",
" Return the number of original observations that correspond to a\n",
" square, redundant distance matrix.\n",
" \n",
" Parameters\n",
" ----------\n",
" d : array_like\n",
" The target distance matrix.\n",
" \n",
" Returns\n",
" -------\n",
" num_obs_dm : int\n",
" The number of observations in the redundant distance matrix.\n",
" \n",
" num_obs_y(Y)\n",
" Return the number of original observations that correspond to a\n",
" condensed distance matrix.\n",
" \n",
" Parameters\n",
" ----------\n",
" Y : array_like\n",
" Condensed distance matrix.\n",
" \n",
" Returns\n",
" -------\n",
" n : int\n",
" The number of observations in the condensed distance matrix `Y`.\n",
" \n",
" pdist(X, metric='euclidean', *, out=None, **kwargs)\n",
" Pairwise distances between observations in n-dimensional space.\n",
" \n",
" See Notes for common calling conventions.\n",
" \n",
" Parameters\n",
" ----------\n",
" X : array_like\n",
" An m by n array of m original observations in an\n",
" n-dimensional space.\n",
" metric : str or function, optional\n",
" The distance metric to use. The distance function can\n",
" be 'braycurtis', 'canberra', 'chebyshev', 'cityblock',\n",
" 'correlation', 'cosine', 'dice', 'euclidean', 'hamming',\n",
" 'jaccard', 'jensenshannon', 'kulczynski1',\n",
" 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto',\n",
" 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',\n",
" 'sqeuclidean', 'yule'.\n",
" out : ndarray\n",
" The output array.\n",
" If not None, condensed distance matrix Y is stored in this array.\n",
" **kwargs : dict, optional\n",
" Extra arguments to `metric`: refer to each metric documentation for a\n",
" list of all possible arguments.\n",
" \n",
" Some possible arguments:\n",
" \n",
" p : scalar\n",
" The p-norm to apply for Minkowski, weighted and unweighted.\n",
" Default: 2.\n",
" \n",
" w : ndarray\n",
" The weight vector for metrics that support weights (e.g., Minkowski).\n",
" \n",
" V : ndarray\n",
" The variance vector for standardized Euclidean.\n",
" Default: var(X, axis=0, ddof=1)\n",
" \n",
" VI : ndarray\n",
" The inverse of the covariance matrix for Mahalanobis.\n",
" Default: inv(cov(X.T)).T\n",
" \n",
" Returns\n",
" -------\n",
" Y : ndarray\n",
" Returns a condensed distance matrix Y. For each :math:`i` and :math:`j`\n",
" (where :math:`i 0` (note\n",
" that this is only a quasi-metric if :math:`0 < p < 1`).\n",
" \n",
" 3. ``Y = pdist(X, 'cityblock')``\n",
" \n",
" Computes the city block or Manhattan distance between the\n",
" points.\n",
" \n",
" 4. ``Y = pdist(X, 'seuclidean', V=None)``\n",
" \n",
" Computes the standardized Euclidean distance. The standardized\n",
" Euclidean distance between two n-vectors ``u`` and ``v`` is\n",
" \n",
" .. math::\n",
" \n",
" \\sqrt{\\sum {(u_i-v_i)^2 / V[x_i]}}\n",
" \n",
" \n",
" V is the variance vector; V[i] is the variance computed over all\n",
" the i'th components of the points. If not passed, it is\n",
" automatically computed.\n",
" \n",
" 5. ``Y = pdist(X, 'sqeuclidean')``\n",
" \n",
" Computes the squared Euclidean distance :math:`\\|u-v\\|_2^2` between\n",
" the vectors.\n",
" \n",
" 6. ``Y = pdist(X, 'cosine')``\n",
" \n",
" Computes the cosine distance between vectors u and v,\n",
" \n",
" .. math::\n",
" \n",
" 1 - \\frac{u \\cdot v}\n",
" {{\\|u\\|}_2 {\\|v\\|}_2}\n",
" \n",
" where :math:`\\|*\\|_2` is the 2-norm of its argument ``*``, and\n",
" :math:`u \\cdot v` is the dot product of ``u`` and ``v``.\n",
" \n",
" 7. ``Y = pdist(X, 'correlation')``\n",
" \n",
" Computes the correlation distance between vectors u and v. This is\n",
" \n",
" .. math::\n",
" \n",
" 1 - \\frac{(u - \\bar{u}) \\cdot (v - \\bar{v})}\n",
" {{\\|(u - \\bar{u})\\|}_2 {\\|(v - \\bar{v})\\|}_2}\n",
" \n",
" where :math:`\\bar{v}` is the mean of the elements of vector v,\n",
" and :math:`x \\cdot y` is the dot product of :math:`x` and :math:`y`.\n",
" \n",
" 8. ``Y = pdist(X, 'hamming')``\n",
" \n",
" Computes the normalized Hamming distance, or the proportion of\n",
" those vector elements between two n-vectors ``u`` and ``v``\n",
" which disagree. To save memory, the matrix ``X`` can be of type\n",
" boolean.\n",
" \n",
" 9. ``Y = pdist(X, 'jaccard')``\n",
" \n",
" Computes the Jaccard distance between the points. Given two\n",
" vectors, ``u`` and ``v``, the Jaccard distance is the\n",
" proportion of those elements ``u[i]`` and ``v[i]`` that\n",
" disagree.\n",
" \n",
" 10. ``Y = pdist(X, 'jensenshannon')``\n",
" \n",
" Computes the Jensen-Shannon distance between two probability arrays.\n",
" Given two probability vectors, :math:`p` and :math:`q`, the\n",
" Jensen-Shannon distance is\n",
" \n",
" .. math::\n",
" \n",
" \\sqrt{\\frac{D(p \\parallel m) + D(q \\parallel m)}{2}}\n",
" \n",
" where :math:`m` is the pointwise mean of :math:`p` and :math:`q`\n",
" and :math:`D` is the Kullback-Leibler divergence.\n",
" \n",
" 11. ``Y = pdist(X, 'chebyshev')``\n",
" \n",
" Computes the Chebyshev distance between the points. The\n",
" Chebyshev distance between two n-vectors ``u`` and ``v`` is the\n",
" maximum norm-1 distance between their respective elements. More\n",
" precisely, the distance is given by\n",
" \n",
" .. math::\n",
" \n",
" d(u,v) = \\max_i {|u_i-v_i|}\n",
" \n",
" 12. ``Y = pdist(X, 'canberra')``\n",
" \n",
" Computes the Canberra distance between the points. The\n",
" Canberra distance between two points ``u`` and ``v`` is\n",
" \n",
" .. math::\n",
" \n",
" d(u,v) = \\sum_i \\frac{|u_i-v_i|}\n",
" {|u_i|+|v_i|}\n",
" \n",
" \n",
" 13. ``Y = pdist(X, 'braycurtis')``\n",
" \n",
" Computes the Bray-Curtis distance between the points. The\n",
" Bray-Curtis distance between two points ``u`` and ``v`` is\n",
" \n",
" \n",
" .. math::\n",
" \n",
" d(u,v) = \\frac{\\sum_i {|u_i-v_i|}}\n",
" {\\sum_i {|u_i+v_i|}}\n",
" \n",
" 14. ``Y = pdist(X, 'mahalanobis', VI=None)``\n",
" \n",
" Computes the Mahalanobis distance between the points. The\n",
" Mahalanobis distance between two points ``u`` and ``v`` is\n",
" :math:`\\sqrt{(u-v)(1/V)(u-v)^T}` where :math:`(1/V)` (the ``VI``\n",
" variable) is the inverse covariance. If ``VI`` is not None,\n",
" ``VI`` will be used as the inverse covariance matrix.\n",
" \n",
" 15. ``Y = pdist(X, 'yule')``\n",
" \n",
" Computes the Yule distance between each pair of boolean\n",
" vectors. (see yule function documentation)\n",
" \n",
" 16. ``Y = pdist(X, 'matching')``\n",
" \n",
" Synonym for 'hamming'.\n",
" \n",
" 17. ``Y = pdist(X, 'dice')``\n",
" \n",
" Computes the Dice distance between each pair of boolean\n",
" vectors. (see dice function documentation)\n",
" \n",
" 18. ``Y = pdist(X, 'kulczynski1')``\n",
" \n",
" Computes the kulczynski1 distance between each pair of\n",
" boolean vectors. (see kulczynski1 function documentation)\n",
" \n",
" 19. ``Y = pdist(X, 'rogerstanimoto')``\n",
" \n",
" Computes the Rogers-Tanimoto distance between each pair of\n",
" boolean vectors. (see rogerstanimoto function documentation)\n",
" \n",
" 20. ``Y = pdist(X, 'russellrao')``\n",
" \n",
" Computes the Russell-Rao distance between each pair of\n",
" boolean vectors. (see russellrao function documentation)\n",
" \n",
" 21. ``Y = pdist(X, 'sokalmichener')``\n",
" \n",
" Computes the Sokal-Michener distance between each pair of\n",
" boolean vectors. (see sokalmichener function documentation)\n",
" \n",
" 22. ``Y = pdist(X, 'sokalsneath')``\n",
" \n",
" Computes the Sokal-Sneath distance between each pair of\n",
" boolean vectors. (see sokalsneath function documentation)\n",
" \n",
" 23. ``Y = pdist(X, 'kulczynski1')``\n",
" \n",
" Computes the Kulczynski 1 distance between each pair of\n",
" boolean vectors. (see kulczynski1 function documentation)\n",
" \n",
" 24. ``Y = pdist(X, f)``\n",
" \n",
" Computes the distance between all pairs of vectors in X\n",
" using the user supplied 2-arity function f. For example,\n",
" Euclidean distance between the vectors could be computed\n",
" as follows::\n",
" \n",
" dm = pdist(X, lambda u, v: np.sqrt(((u-v)**2).sum()))\n",
" \n",
" Note that you should avoid passing a reference to one of\n",
" the distance functions defined in this library. For example,::\n",
" \n",
" dm = pdist(X, sokalsneath)\n",
" \n",
" would calculate the pair-wise distances between the vectors in\n",
" X using the Python function sokalsneath. This would result in\n",
" sokalsneath being called :math:`{n \\choose 2}` times, which\n",
" is inefficient. Instead, the optimized C version is more\n",
" efficient, and we call it using the following syntax.::\n",
" \n",
" dm = pdist(X, 'sokalsneath')\n",
" \n",
" Examples\n",
" --------\n",
" >>> import numpy as np\n",
" >>> from scipy.spatial.distance import pdist\n",
" \n",
" ``x`` is an array of five points in three-dimensional space.\n",
" \n",
" >>> x = np.array([[2, 0, 2], [2, 2, 3], [-2, 4, 5], [0, 1, 9], [2, 2, 4]])\n",
" \n",
" ``pdist(x)`` with no additional arguments computes the 10 pairwise\n",
" Euclidean distances:\n",
" \n",
" >>> pdist(x)\n",
" array([2.23606798, 6.40312424, 7.34846923, 2.82842712, 4.89897949,\n",
" 6.40312424, 1. , 5.38516481, 4.58257569, 5.47722558])\n",
" \n",
" The following computes the pairwise Minkowski distances with ``p = 3.5``:\n",
" \n",
" >>> pdist(x, metric='minkowski', p=3.5)\n",
" array([2.04898923, 5.1154929 , 7.02700737, 2.43802731, 4.19042714,\n",
" 6.03956994, 1. , 4.45128103, 4.10636143, 5.0619695 ])\n",
" \n",
" The pairwise city block or Manhattan distances:\n",
" \n",
" >>> pdist(x, metric='cityblock')\n",
" array([ 3., 11., 10., 4., 8., 9., 1., 9., 7., 8.])\n",
" \n",
" rogerstanimoto(u, v, w=None)\n",
" Compute the Rogers-Tanimoto dissimilarity between two boolean 1-D arrays.\n",
" \n",
" The Rogers-Tanimoto dissimilarity between two boolean 1-D arrays\n",
" `u` and `v`, is defined as\n",
" \n",
" .. math::\n",
" \\frac{R}\n",
" {c_{TT} + c_{FF} + R}\n",
" \n",
" where :math:`c_{ij}` is the number of occurrences of\n",
" :math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for\n",
" :math:`k < n` and :math:`R = 2(c_{TF} + c_{FT})`.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like, bool\n",
" Input array.\n",
" v : (N,) array_like, bool\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" rogerstanimoto : double\n",
" The Rogers-Tanimoto dissimilarity between vectors\n",
" `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.rogerstanimoto([1, 0, 0], [0, 1, 0])\n",
" 0.8\n",
" >>> distance.rogerstanimoto([1, 0, 0], [1, 1, 0])\n",
" 0.5\n",
" >>> distance.rogerstanimoto([1, 0, 0], [2, 0, 0])\n",
" -1.0\n",
" \n",
" russellrao(u, v, w=None)\n",
" Compute the Russell-Rao dissimilarity between two boolean 1-D arrays.\n",
" \n",
" The Russell-Rao dissimilarity between two boolean 1-D arrays, `u` and\n",
" `v`, is defined as\n",
" \n",
" .. math::\n",
" \n",
" \\frac{n - c_{TT}}\n",
" {n}\n",
" \n",
" where :math:`c_{ij}` is the number of occurrences of\n",
" :math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for\n",
" :math:`k < n`.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like, bool\n",
" Input array.\n",
" v : (N,) array_like, bool\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" russellrao : double\n",
" The Russell-Rao dissimilarity between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.russellrao([1, 0, 0], [0, 1, 0])\n",
" 1.0\n",
" >>> distance.russellrao([1, 0, 0], [1, 1, 0])\n",
" 0.6666666666666666\n",
" >>> distance.russellrao([1, 0, 0], [2, 0, 0])\n",
" 0.3333333333333333\n",
" \n",
" seuclidean(u, v, V)\n",
" Return the standardized Euclidean distance between two 1-D arrays.\n",
" \n",
" The standardized Euclidean distance between two n-vectors `u` and `v` is\n",
" \n",
" .. math::\n",
" \n",
" \\sqrt{\\sum\\limits_i \\frac{1}{V_i} \\left(u_i-v_i \\right)^2}\n",
" \n",
" ``V`` is the variance vector; ``V[I]`` is the variance computed over all the i-th\n",
" components of the points. If not passed, it is automatically computed.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input array.\n",
" v : (N,) array_like\n",
" Input array.\n",
" V : (N,) array_like\n",
" `V` is an 1-D array of component variances. It is usually computed\n",
" among a larger collection vectors.\n",
" \n",
" Returns\n",
" -------\n",
" seuclidean : double\n",
" The standardized Euclidean distance between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.seuclidean([1, 0, 0], [0, 1, 0], [0.1, 0.1, 0.1])\n",
" 4.4721359549995796\n",
" >>> distance.seuclidean([1, 0, 0], [0, 1, 0], [1, 0.1, 0.1])\n",
" 3.3166247903553998\n",
" >>> distance.seuclidean([1, 0, 0], [0, 1, 0], [10, 0.1, 0.1])\n",
" 3.1780497164141406\n",
" \n",
" sokalmichener(u, v, w=None)\n",
" Compute the Sokal-Michener dissimilarity between two boolean 1-D arrays.\n",
" \n",
" The Sokal-Michener dissimilarity between boolean 1-D arrays `u` and `v`,\n",
" is defined as\n",
" \n",
" .. math::\n",
" \n",
" \\frac{R}\n",
" {S + R}\n",
" \n",
" where :math:`c_{ij}` is the number of occurrences of\n",
" :math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for\n",
" :math:`k < n`, :math:`R = 2 * (c_{TF} + c_{FT})` and\n",
" :math:`S = c_{FF} + c_{TT}`.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like, bool\n",
" Input array.\n",
" v : (N,) array_like, bool\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" sokalmichener : double\n",
" The Sokal-Michener dissimilarity between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.sokalmichener([1, 0, 0], [0, 1, 0])\n",
" 0.8\n",
" >>> distance.sokalmichener([1, 0, 0], [1, 1, 0])\n",
" 0.5\n",
" >>> distance.sokalmichener([1, 0, 0], [2, 0, 0])\n",
" -1.0\n",
" \n",
" sokalsneath(u, v, w=None)\n",
" Compute the Sokal-Sneath dissimilarity between two boolean 1-D arrays.\n",
" \n",
" The Sokal-Sneath dissimilarity between `u` and `v`,\n",
" \n",
" .. math::\n",
" \n",
" \\frac{R}\n",
" {c_{TT} + R}\n",
" \n",
" where :math:`c_{ij}` is the number of occurrences of\n",
" :math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for\n",
" :math:`k < n` and :math:`R = 2(c_{TF} + c_{FT})`.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like, bool\n",
" Input array.\n",
" v : (N,) array_like, bool\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" sokalsneath : double\n",
" The Sokal-Sneath dissimilarity between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.sokalsneath([1, 0, 0], [0, 1, 0])\n",
" 1.0\n",
" >>> distance.sokalsneath([1, 0, 0], [1, 1, 0])\n",
" 0.66666666666666663\n",
" >>> distance.sokalsneath([1, 0, 0], [2, 1, 0])\n",
" 0.0\n",
" >>> distance.sokalsneath([1, 0, 0], [3, 1, 0])\n",
" -2.0\n",
" \n",
" sqeuclidean(u, v, w=None)\n",
" Compute the squared Euclidean distance between two 1-D arrays.\n",
" \n",
" The squared Euclidean distance between `u` and `v` is defined as\n",
" \n",
" .. math::\n",
" \n",
" \\sum_i{w_i |u_i - v_i|^2}\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like\n",
" Input array.\n",
" v : (N,) array_like\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" sqeuclidean : double\n",
" The squared Euclidean distance between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.sqeuclidean([1, 0, 0], [0, 1, 0])\n",
" 2.0\n",
" >>> distance.sqeuclidean([1, 1, 0], [0, 1, 0])\n",
" 1.0\n",
" \n",
" squareform(X, force='no', checks=True)\n",
" Convert a vector-form distance vector to a square-form distance\n",
" matrix, and vice-versa.\n",
" \n",
" Parameters\n",
" ----------\n",
" X : array_like\n",
" Either a condensed or redundant distance matrix.\n",
" force : str, optional\n",
" As with MATLAB(TM), if force is equal to ``'tovector'`` or\n",
" ``'tomatrix'``, the input will be treated as a distance matrix or\n",
" distance vector respectively.\n",
" checks : bool, optional\n",
" If set to False, no checks will be made for matrix\n",
" symmetry nor zero diagonals. This is useful if it is known that\n",
" ``X - X.T1`` is small and ``diag(X)`` is close to zero.\n",
" These values are ignored any way so they do not disrupt the\n",
" squareform transformation.\n",
" \n",
" Returns\n",
" -------\n",
" Y : ndarray\n",
" If a condensed distance matrix is passed, a redundant one is\n",
" returned, or if a redundant one is passed, a condensed distance\n",
" matrix is returned.\n",
" \n",
" Notes\n",
" -----\n",
" 1. ``v = squareform(X)``\n",
" \n",
" Given a square n-by-n symmetric distance matrix ``X``,\n",
" ``v = squareform(X)`` returns a ``n * (n-1) / 2``\n",
" (i.e. binomial coefficient n choose 2) sized vector `v`\n",
" where :math:`v[{n \\choose 2} - {n-i \\choose 2} + (j-i-1)]`\n",
" is the distance between distinct points ``i`` and ``j``.\n",
" If ``X`` is non-square or asymmetric, an error is raised.\n",
" \n",
" 2. ``X = squareform(v)``\n",
" \n",
" Given a ``n * (n-1) / 2`` sized vector ``v``\n",
" for some integer ``n >= 1`` encoding distances as described,\n",
" ``X = squareform(v)`` returns a n-by-n distance matrix ``X``.\n",
" The ``X[i, j]`` and ``X[j, i]`` values are set to\n",
" :math:`v[{n \\choose 2} - {n-i \\choose 2} + (j-i-1)]`\n",
" and all diagonal elements are zero.\n",
" \n",
" In SciPy 0.19.0, ``squareform`` stopped casting all input types to\n",
" float64, and started returning arrays of the same dtype as the input.\n",
" \n",
" Examples\n",
" --------\n",
" >>> import numpy as np\n",
" >>> from scipy.spatial.distance import pdist, squareform\n",
" \n",
" ``x`` is an array of five points in three-dimensional space.\n",
" \n",
" >>> x = np.array([[2, 0, 2], [2, 2, 3], [-2, 4, 5], [0, 1, 9], [2, 2, 4]])\n",
" \n",
" ``pdist(x)`` computes the Euclidean distances between each pair of\n",
" points in ``x``. The distances are returned in a one-dimensional\n",
" array with length ``5*(5 - 1)/2 = 10``.\n",
" \n",
" >>> distvec = pdist(x)\n",
" >>> distvec\n",
" array([2.23606798, 6.40312424, 7.34846923, 2.82842712, 4.89897949,\n",
" 6.40312424, 1. , 5.38516481, 4.58257569, 5.47722558])\n",
" \n",
" ``squareform(distvec)`` returns the 5x5 distance matrix.\n",
" \n",
" >>> m = squareform(distvec)\n",
" >>> m\n",
" array([[0. , 2.23606798, 6.40312424, 7.34846923, 2.82842712],\n",
" [2.23606798, 0. , 4.89897949, 6.40312424, 1. ],\n",
" [6.40312424, 4.89897949, 0. , 5.38516481, 4.58257569],\n",
" [7.34846923, 6.40312424, 5.38516481, 0. , 5.47722558],\n",
" [2.82842712, 1. , 4.58257569, 5.47722558, 0. ]])\n",
" \n",
" When given a square distance matrix ``m``, ``squareform(m)`` returns\n",
" the one-dimensional condensed distance vector associated with the\n",
" matrix. In this case, we recover ``distvec``.\n",
" \n",
" >>> squareform(m)\n",
" array([2.23606798, 6.40312424, 7.34846923, 2.82842712, 4.89897949,\n",
" 6.40312424, 1. , 5.38516481, 4.58257569, 5.47722558])\n",
" \n",
" yule(u, v, w=None)\n",
" Compute the Yule dissimilarity between two boolean 1-D arrays.\n",
" \n",
" The Yule dissimilarity is defined as\n",
" \n",
" .. math::\n",
" \n",
" \\frac{R}{c_{TT} * c_{FF} + \\frac{R}{2}}\n",
" \n",
" where :math:`c_{ij}` is the number of occurrences of\n",
" :math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for\n",
" :math:`k < n` and :math:`R = 2.0 * c_{TF} * c_{FT}`.\n",
" \n",
" Parameters\n",
" ----------\n",
" u : (N,) array_like, bool\n",
" Input array.\n",
" v : (N,) array_like, bool\n",
" Input array.\n",
" w : (N,) array_like, optional\n",
" The weights for each value in `u` and `v`. Default is None,\n",
" which gives each value a weight of 1.0\n",
" \n",
" Returns\n",
" -------\n",
" yule : double\n",
" The Yule dissimilarity between vectors `u` and `v`.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.spatial import distance\n",
" >>> distance.yule([1, 0, 0], [0, 1, 0])\n",
" 2.0\n",
" >>> distance.yule([1, 1, 0], [0, 1, 0])\n",
" 0.0\n",
"\n",
"DATA\n",
" __all__ = ['braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock'...\n",
"\n",
"FILE\n",
" c:\\users\\mertg\\anaconda3\\lib\\site-packages\\scipy\\spatial\\distance.py\n",
"\n",
"\n"
]
}
],
"source": [
"import scipy.spatial.distance\n",
"help(scipy.spatial.distance)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# k-means clustering \n",
"\n",
"In *k-means clustering* we are given a set of $d$-dimensional vectors and we want to identify *k* sets $S_i$ such that\n",
"\n",
"$$\\sum_{i=0}^k \\sum_{x_j \\in S_i} ||x_j - \\mu_i||^2$$\n",
"is **minimized** where $\\mu_i$ is the *mean* of cluster $S_i$. That is, all points are close as possible to the 'center' of the cluster.\n",
"\n",
"**Limitations**\n",
"\n",
" * Classical k-means requires that we be able to take an average of our points - *no arbitrary distance functions*.\n",
" * Must provide $k$ as a parameter - bad $k$, bad clustering.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"# k-means clustering \n",
"\n",
"\n",
"**General algorithm**\n",
"\n",
" * Choose initial set of $k$ cluster centers (centroids/means).\n",
" * Compute means of these clusters.\n",
" * Reassign points using updated means.\n",
" * Repeat\n",
"\n",
"Will converge to **local** optimum.\n",
"\n",
"https://bits.csb.pitt.edu/kmeans/"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# scipy vector quantization\n",
"\n",
"First let's make a toy data set..."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import scipy.cluster.vq as vq #vq: vector quantization\n",
"import numpy as np\n",
"import matplotlib.pylab as plt\n",
"%matplotlib inline\n",
"randpts1 = np.random.randn(100,2)/(4,1) #100 integer coordinates in the range [0:50],[0:50]\n",
"randpts2 = (np.random.randn(100,2)+(1,0))/(1,4)\n",
"plt.plot(randpts1[:,0],randpts1[:,1],'o',randpts2[:,0],randpts2[:,1],'o')\n",
"randpts = np.vstack((randpts1,randpts2))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# scipy vector quantization"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"(means,clusters) = vq.kmeans2(randpts,2)#returns tuple of means and cluster assignments"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The means are the cluster centers"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(randpts[:,0],randpts[:,1],c=clusters)\n",
"plt.plot(means[:,0],means[:,1],'*',ms=20,c='red');"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Changing k"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(means,clusters) = vq.kmeans2(randpts,3)\n",
"plt.scatter(randpts[:,0],randpts[:,1],c=clusters)\n",
"plt.plot(means[:,0],means[:,1],'*',ms=20,c='red');"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Changing k"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(means,clusters) = vq.kmeans2(randpts,4)\n",
"plt.scatter(randpts[:,0],randpts[:,1],c=clusters)\n",
"plt.plot(means[:,0],means[:,1],'*',ms=20,c='red');"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%html\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# scipy vector quantization\n",
"\n",
"Vector quantization is just a fancy way to describe assigning clusters to new points."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"newrand = np.random.randn(100,2)\n",
"code,dist = vq.vq(newrand,means)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(newrand[:,0],newrand[:,1],c=code)\n",
"plt.plot(means[:,0],means[:,1],'*',ms=20);"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%html\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Hierarchical Clustering\n",
"\n",
"Hierarchical clustering creates a heirarchy, where each cluster is formed from subclusters. \n",
"\n",
"
\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Agglomerative clustering\n",
"\n",
"Agglomerative builds this hierarchy from the *bottom up*: start with all singleton clusters, find the two clusters that are closest, combine them into a cluster, repeat.\n",
"\n",
"This requires there be a notion of distance between *clusters of items*, not just the items themselves.\n",
"\n",
"**Important:** All you need is a distance function - you do *not* need to be able to take an average (as with k-means). "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Distance (Linkage) Methods\n",
"\n",
"
\n",
"\n",
"* **average**: \n",
" $$d(u,v) = \\sum_{ij}\\frac{d(u_i,v_j)}{|u||v|}$$\n",
"* **complete** or farthest point: \n",
" $$d(u,v) = \\max(dist(u_i,v_j))$$\n",
"* **single** or nearest point: \n",
" $$d(u,v) = \\min(dist(u_i,v_j))$$\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%html\n",
"\n",
"
\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# `linkage`\n",
"\n",
"`scipy.cluster.hierarchy.linkage` creates a clustering hierarchy. It takes three parameters:\n",
" * *y* the data or a precalculated distance matrix\n",
" * *method* the linkage method (default single)\n",
" * *metric* the distance metric to use (default euclidean)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"import scipy.cluster.hierarchy as hclust\n",
"linkage_matrix = hclust.linkage(randpts) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" A (n−1) by 4 matrix Z is returned. At the i-th iteration, clusters with indices Z[i, 0] and Z[i, 1] are combined to form cluster n+i. A cluster with an index less than n corresponds to one of the n original observations. The distance between clusters Z[i, 0] and Z[i, 1] is given by Z[i, 2]. The fourth value Z[i, 3] represents the number of original observations in the newly formed cluster."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(199, 4)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linkage_matrix.shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1.49000000e+02, 1.71000000e+02, 4.46097932e-03, 2.00000000e+00],\n",
" [4.60000000e+01, 7.20000000e+01, 6.85261819e-03, 2.00000000e+00],\n",
" [1.60000000e+01, 7.30000000e+01, 1.51521318e-02, 2.00000000e+00],\n",
" [1.37000000e+02, 1.57000000e+02, 1.53148351e-02, 2.00000000e+00],\n",
" [1.19000000e+02, 1.89000000e+02, 1.78213509e-02, 2.00000000e+00],\n",
" [1.22000000e+02, 1.98000000e+02, 2.04763202e-02, 2.00000000e+00],\n",
" [1.34000000e+02, 2.00000000e+02, 2.35915430e-02, 3.00000000e+00],\n",
" [1.38000000e+02, 1.87000000e+02, 2.65878878e-02, 2.00000000e+00],\n",
" [2.40000000e+01, 6.90000000e+01, 2.93336673e-02, 2.00000000e+00],\n",
" [6.00000000e+01, 9.60000000e+01, 2.99804179e-02, 2.00000000e+00],\n",
" [1.82000000e+02, 1.85000000e+02, 3.14543289e-02, 2.00000000e+00],\n",
" [1.54000000e+02, 1.96000000e+02, 3.38672911e-02, 2.00000000e+00],\n",
" [9.00000000e+00, 1.76000000e+02, 3.54702089e-02, 2.00000000e+00],\n",
" [3.90000000e+01, 2.12000000e+02, 3.56342391e-02, 3.00000000e+00],\n",
" [2.03000000e+02, 2.09000000e+02, 3.78817319e-02, 4.00000000e+00],\n",
" [1.15000000e+02, 1.47000000e+02, 4.09620150e-02, 2.00000000e+00],\n",
" [3.00000000e+00, 1.74000000e+02, 4.09881290e-02, 2.00000000e+00],\n",
" [6.00000000e+00, 9.90000000e+01, 4.14929659e-02, 2.00000000e+00],\n",
" [9.00000000e+01, 1.72000000e+02, 4.16513026e-02, 2.00000000e+00],\n",
" [1.40000000e+01, 7.60000000e+01, 4.26578945e-02, 2.00000000e+00],\n",
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" [3.93000000e+02, 3.94000000e+02, 7.69909958e-01, 1.97000000e+02],\n",
" [8.80000000e+01, 9.30000000e+01, 8.46319094e-01, 2.00000000e+00],\n",
" [8.60000000e+01, 3.95000000e+02, 8.79918242e-01, 1.98000000e+02],\n",
" [3.96000000e+02, 3.97000000e+02, 9.84658940e-01, 2.00000000e+02]])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linkage_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Dendograms"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"hclust.dendrogram(linkage_matrix,p=10,truncate_mode='level',no_labels=True);#show first 10 levels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*The cophenetic distance between two observations that have been clustered is defined to be the intergroup dissimilarity at which the two observations are first combined into a single cluster. It is shown as the height of the U-links.*"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# `fcluster`: extracting clusters from a hierarchy\n",
"\n",
"`fcluster` takes a linkage matrix and returns a cluster assignment. It takes a threshold value and a string specifying what method to use to form the cluster."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function fcluster in module scipy.cluster.hierarchy:\n",
"\n",
"fcluster(Z, t, criterion='inconsistent', depth=2, R=None, monocrit=None)\n",
" Form flat clusters from the hierarchical clustering defined by\n",
" the given linkage matrix.\n",
" \n",
" Parameters\n",
" ----------\n",
" Z : ndarray\n",
" The hierarchical clustering encoded with the matrix returned\n",
" by the `linkage` function.\n",
" t : scalar\n",
" For criteria 'inconsistent', 'distance' or 'monocrit',\n",
" this is the threshold to apply when forming flat clusters.\n",
" For 'maxclust' or 'maxclust_monocrit' criteria,\n",
" this would be max number of clusters requested.\n",
" criterion : str, optional\n",
" The criterion to use in forming flat clusters. This can\n",
" be any of the following values:\n",
" \n",
" ``inconsistent`` :\n",
" If a cluster node and all its\n",
" descendants have an inconsistent value less than or equal\n",
" to `t`, then all its leaf descendants belong to the\n",
" same flat cluster. When no non-singleton cluster meets\n",
" this criterion, every node is assigned to its own\n",
" cluster. (Default)\n",
" \n",
" ``distance`` :\n",
" Forms flat clusters so that the original\n",
" observations in each flat cluster have no greater a\n",
" cophenetic distance than `t`.\n",
" \n",
" ``maxclust`` :\n",
" Finds a minimum threshold ``r`` so that\n",
" the cophenetic distance between any two original\n",
" observations in the same flat cluster is no more than\n",
" ``r`` and no more than `t` flat clusters are formed.\n",
" \n",
" ``monocrit`` :\n",
" Forms a flat cluster from a cluster node c\n",
" with index i when ``monocrit[j] <= t``.\n",
" \n",
" For example, to threshold on the maximum mean distance\n",
" as computed in the inconsistency matrix R with a\n",
" threshold of 0.8 do::\n",
" \n",
" MR = maxRstat(Z, R, 3)\n",
" fcluster(Z, t=0.8, criterion='monocrit', monocrit=MR)\n",
" \n",
" ``maxclust_monocrit`` :\n",
" Forms a flat cluster from a\n",
" non-singleton cluster node ``c`` when ``monocrit[i] <=\n",
" r`` for all cluster indices ``i`` below and including\n",
" ``c``. ``r`` is minimized such that no more than ``t``\n",
" flat clusters are formed. monocrit must be\n",
" monotonic. For example, to minimize the threshold t on\n",
" maximum inconsistency values so that no more than 3 flat\n",
" clusters are formed, do::\n",
" \n",
" MI = maxinconsts(Z, R)\n",
" fcluster(Z, t=3, criterion='maxclust_monocrit', monocrit=MI)\n",
" depth : int, optional\n",
" The maximum depth to perform the inconsistency calculation.\n",
" It has no meaning for the other criteria. Default is 2.\n",
" R : ndarray, optional\n",
" The inconsistency matrix to use for the 'inconsistent'\n",
" criterion. This matrix is computed if not provided.\n",
" monocrit : ndarray, optional\n",
" An array of length n-1. `monocrit[i]` is the\n",
" statistics upon which non-singleton i is thresholded. The\n",
" monocrit vector must be monotonic, i.e., given a node c with\n",
" index i, for all node indices j corresponding to nodes\n",
" below c, ``monocrit[i] >= monocrit[j]``.\n",
" \n",
" Returns\n",
" -------\n",
" fcluster : ndarray\n",
" An array of length ``n``. ``T[i]`` is the flat cluster number to\n",
" which original observation ``i`` belongs.\n",
" \n",
" See Also\n",
" --------\n",
" linkage : for information about hierarchical clustering methods work.\n",
" \n",
" Examples\n",
" --------\n",
" >>> from scipy.cluster.hierarchy import ward, fcluster\n",
" >>> from scipy.spatial.distance import pdist\n",
" \n",
" All cluster linkage methods - e.g., `scipy.cluster.hierarchy.ward`\n",
" generate a linkage matrix ``Z`` as their output:\n",
" \n",
" >>> X = [[0, 0], [0, 1], [1, 0],\n",
" ... [0, 4], [0, 3], [1, 4],\n",
" ... [4, 0], [3, 0], [4, 1],\n",
" ... [4, 4], [3, 4], [4, 3]]\n",
" \n",
" >>> Z = ward(pdist(X))\n",
" \n",
" >>> Z\n",
" array([[ 0. , 1. , 1. , 2. ],\n",
" [ 3. , 4. , 1. , 2. ],\n",
" [ 6. , 7. , 1. , 2. ],\n",
" [ 9. , 10. , 1. , 2. ],\n",
" [ 2. , 12. , 1.29099445, 3. ],\n",
" [ 5. , 13. , 1.29099445, 3. ],\n",
" [ 8. , 14. , 1.29099445, 3. ],\n",
" [11. , 15. , 1.29099445, 3. ],\n",
" [16. , 17. , 5.77350269, 6. ],\n",
" [18. , 19. , 5.77350269, 6. ],\n",
" [20. , 21. , 8.16496581, 12. ]])\n",
" \n",
" This matrix represents a dendrogram, where the first and second elements\n",
" are the two clusters merged at each step, the third element is the\n",
" distance between these clusters, and the fourth element is the size of\n",
" the new cluster - the number of original data points included.\n",
" \n",
" `scipy.cluster.hierarchy.fcluster` can be used to flatten the\n",
" dendrogram, obtaining as a result an assignation of the original data\n",
" points to single clusters.\n",
" \n",
" This assignation mostly depends on a distance threshold ``t`` - the maximum\n",
" inter-cluster distance allowed:\n",
" \n",
" >>> fcluster(Z, t=0.9, criterion='distance')\n",
" array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=int32)\n",
" \n",
" >>> fcluster(Z, t=1.1, criterion='distance')\n",
" array([1, 1, 2, 3, 3, 4, 5, 5, 6, 7, 7, 8], dtype=int32)\n",
" \n",
" >>> fcluster(Z, t=3, criterion='distance')\n",
" array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4], dtype=int32)\n",
" \n",
" >>> fcluster(Z, t=9, criterion='distance')\n",
" array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)\n",
" \n",
" In the first case, the threshold ``t`` is too small to allow any two\n",
" samples in the data to form a cluster, so 12 different clusters are\n",
" returned.\n",
" \n",
" In the second case, the threshold is large enough to allow the first\n",
" 4 points to be merged with their nearest neighbors. So, here, only 8\n",
" clusters are returned.\n",
" \n",
" The third case, with a much higher threshold, allows for up to 8 data\n",
" points to be connected - so 4 clusters are returned here.\n",
" \n",
" Lastly, the threshold of the fourth case is large enough to allow for\n",
" all data points to be merged together - so a single cluster is returned.\n",
"\n"
]
}
],
"source": [
"help(hclust.fcluster)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Flatten based on distance threshold"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
"14"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clusters = hclust.fcluster(linkage_matrix,0.3,'distance')\n",
"len(set(clusters))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(randpts[:,0],randpts[:,1],c=clusters);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Flatten based on number of clusters"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clusters = hclust.fcluster(linkage_matrix,4,'maxclust')\n",
"len(set(clusters))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(randpts[:,0],randpts[:,1],c=clusters)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# `fclusterdata`\n",
"\n",
"`fclusterdata` does both linkage and fcluster in one step. Let's try out different linkage methods."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"14"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"clusters = hclust.fclusterdata(randpts,.3,criterion='distance',method='single')\n",
"plt.scatter(randpts[:,0],randpts[:,1],c=clusters)\n",
"len(set(clusters))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"63"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"clusters = hclust.fclusterdata(randpts,.3,criterion='distance',method='complete')\n",
"plt.scatter(randpts[:,0],randpts[:,1],c=clusters);\n",
"len(set(clusters))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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JBC6+9zyi4iIBa/nrwAv78N8/nue0s3tU6nxfPD+F6zrcyTcvzuD3KUuZ/ek8Hhn1HPcO/hdZadkeXxebFMN/Fjxb4TSQYbcx6sYzS3YRnjlxboVJSDGb3cbsT+dXKm4hhBA1k3RWrUfcLjc5GbmER4URFhFW6dfP/mw+L17/ZoXPGTaDnmd04cU5T5Z7zjRN/vxpJb98/Bt/zVlDfnZB2QMUoOHi+87n5hevYVTIFd4DUXD6RX15aupDlf4ahBBCBJ50VhUVstltZQpCK0NrzZf/N9VrT5KVv61j28qdtO/VpuRxR76Dp8a+xPJZqz0v3y16aMp/fmD4FacTmxRD1lHPoys2m43kZkkn9HUIIYSoWWRqRvjl4K7D7N28v8IkpJjNbrD4++VlHnv3H5+yYs4aAK/Ld4tf/+P7czh3/FkV1pIUc7vcnPO34f4HX0WO7DvKzrW7vU5BCSGEqBwZERF+KSxw+j5IKZyOY8dlpWXz84e/+UxAirldJrvX7+Hp6Q/x65cLOXogHfO4vWWUghHXD6PDaW0rFf/JWDV3HR//66uSAlnDZjB4XD/GT7iGJm1lR18hhDgZMiIi/NK4dQMior1vjud2uml36rFpmfWLNuMq9NzD5HjKUETHRxHfII7/LnqePuecatWPFImIDueqRy/m/vdvrWz4J+yP75bx0Ihn2Lh0a8ljptvk92l/cke/h9m//WC1xSKEEHWRjIgIv4RFhDH6prOY/ubPFa5oMQxFTFIMgy461pPE28qXimhTM+zy0wFo0DyJ579/hEO7U9m+ehchYSF0G9y52nYKBnAWOnnlpndA63JTUqbLJDczj3fun8izMx6utpiEEKKukURE+O26py9j9fz17Fizu8x0i81uYNhtPP7N/WV6knTq287r/jKlGXaDxq0acMZlA8s83qhVAxq1alB1X0QlLPl+hdeiWdNtsvSHv0g7mE5i44RqjEwIIeoOmZoRfouKjeQ/C57h+qcvJ6lZIgD2UDtnXjWEt/58gZ7DupY5PrlZEoPH9vNaeFqsTbeWvPTbU4SGhwYk9hOxb+sBbB660hbTWnNw5+FqikgIIeoeGRERlRIRHcHVj13M1Y9djLPQiT3EjlKeW7ff884t7N6wlz2b9qPRJUt1lVJExkYw4rozGDyuPz2GdvF6npO1c10KK2atxu0y6dyvPT3O8H29qLhITLfv0ZziRnFCCCEqTxqaiYDLz8nn5w9+48f353B0fxoJjeIZfdOZnHfL2UTFRQX02llHs3n+qtf4a/YaDEOBUphukxadm/Lkt/+gVZcWHl+bdjCdK1veWm7lTjGloHmnpny4/rWAJlFCCFHbVOb+LYmIqLNcThd3DXiUHWt2lyucNWwGUXGRvLf6Za/N0d6+92Omv/GTx/4pT3z7D4aM61+VYQshRK1Xmfu31IiIOmvR9GVsW7mzwtU7ptta9TL9jZ+9nuPvL1/HhbePQimFYSjsITaUgrCIUO5//1ZJQoQQ4iTJiIios566+CUWf7fMa51HYpMEvtn3ns9zHd5zhAWTF5N1NJsmbRtxxmWDiIyJqMpwhRCizpC9ZoQAso5k+yw2zc3I9etcDVskc8n9F1RFWEIIIUqRqRlRZzVt18j78lsFjVo3rL6AhBBClCOJiKizRo8/G7eHFS9gdY8//+8jqi8gIYQQ5UgiIuqsLgM7MuqmMyt8zrAZdOzTnnNvPquaoxJCCFGaJCKizlJKcd+7f2f8C9cQ1+BYsVRYRCgX3DqSl359grCIsCBGKIQQQlbNiHrB5XSxc20Kbpeblqc0lxUvQggRQNJHRIjj2EPsNO/YhIiYCHL8XCkjhBAi8GT5rqjz0g9n8vFjXzLn8wU4HS4AOpzWluueuowB5/cOcnRCCFG/ydRMHXRgxyEWTllCXnY+zTs2ZcjF/ettLUT64UzuGvAIqXuOlumwqgyFNjUPfHg7o/42PIgRCiFE3VNjpmYWLFjABRdcQNOmTVFKMX369EBert4rdDh58YY3ua7DnXz4yBd8/e/p/Pu6N7i86S0snLo02OEFxWdPTSqXhABo08q/X7/9fZmqEUKIIApoIpKbm0vPnj158803A3kZUeTV8e8w5/MFoME0NW6nG4DcrDyevewVVs9bH+QIq5cj38GsT+ZVuNdMMVehi1+/WFiNUQkhhCgtoDUio0ePZvTo0YG8hCiyd8t+zzdUbW1Z/8lT3/DqvGeqN7AgSjuYgSO/0OsxNrvBvq0HqikiIYQQx6tRxaoOhwOHw1HyeVZWVhCjqV3mT1qMYTM8vvs3Tc3aBRtJP5RBQqP46g0uSKJiI30eo7UmMlaW8gohRLDUqERkwoQJPP3008EOo1bKycjFMBSm2/txuVn5AU9E0g9l8POHv7FmvjUV1HNYN0bddCYJDeMCet3jxSbF0HNYV9Yu3OgxQXO7TM64bFC1xiWEEOKYGtVH5JFHHiEzM7PkY8+ePcEOqdZo1qEJLpf3LCQkzE5Sk/iAxrFs5iquaXsHE5/4mhWz17Bi9ho+fvwrrmlzO8tmrgrotSty7ZOXorW2NpY5jmEoBo/rR5tuLas9LiGEEJYalYiEhYURGxtb5kP4Z/gVgwgND/X4vGE3OOvqoUREB24a4sDOQzw55t84C5wlq1LAWqHiLHDy5NgXObjrcMCuX5GeZ3Tl8UkPlHRStYfYMGzWr/3gi/vzz0/vrtZ4hBBClFWjpmbEiYuKi+Lut8bz8o1vl/TIKGbYDBIbxXPDs1cE5Nput5t9Ww8y6cXpuJxuKmpNo7W1iueH/81i/AvXBCQOT4aM60/fUaeyYPJi9mzaR3h0OEMvGUCLTs2qNQ4hhBDlBTQRycnJYdu2bSWf79y5k1WrVpGYmEjLljIcXtXOuWE4sUkxfPLE12xfvRuwRgDOuHwQ41+4hqQmCVV6PdM0mf76z0x6eQZH96f7Pt5tsvj75dWeiACER4Yx8vph1X5dIYQQ3gW0s+q8efMYPrx818rrr7+eiRMn+ny9dFY9cQd3HSYvK5+GLZOJjo+q8vNrrXnttvf46b05lXpd03aN+WTrG1UejxBCiJqjMvfvgI6IDBs2rMJhehF4jVs3DOj51y/aVOkkxGY36Hp6pwBFJIQINqfLzW8rt/HXtr0AnNa+OWf2ak+I3RbkyERNJjUi4oT8+P4cbHYDt8tz19LjuV0mF90xKoBRCSGCZfOew9z11nSOZOZiN6yC8MkL1pAUG8kbd46lc4vAvjkStVeNWjUjao89m/b5nYQYduvX7OZ/X0Onvu0DGZYQIgjSsvL4+2vfkpadB4DLNHGZ1t+H9Jx8bn3tW9Ky8oIZoqjBZESkDjt6IJ0Vs1bjKnTR/rQ2dOzdrsrOHZMYXW51TjkKwqPCOXVYV8bdex69zuxeZdcXQtQcUxetJSe/ELOCqXjT1OTkFzJ10VrGj+4fhOhETSeJSB3kyHfw+h0fMOezBWU6inY4rQ2PfHFPlSxbHX7FYJbPXO3xecNmcPlDF3Hj81ed9LWEEDXb7BVbKkxCiplaM2v5ZklERIVkaqaO0Vrzj7OeZtbE8rvObl+9m3sHP07q3qMnfZ1hlw+ieccm2Ozlf4UMm0FUXCQXVrIeRGsX2syRAmchapk8h9PnMfmFvo8R9ZMkInXM63e8z6YlWyt8znSb5GTkMuXV70/6OqHhobz021N0OK0tYNWB2Ioq4xu1asArc58iuWmiX+fSzo2Y6feiD3VHHz4NfXgAZvZraDPnpOMUQgRex+bJ2IwK9lEoYjMUHZo1qMaIRG0S0D4iJ0v6iFTOhiVbuGfQYz6Pi46PYlraxCq5ptaajUu38tfsNbhdbroM6kTvET0wDP9yXO34A51+C+Au+ihmgL0tKvErlFG9m+UJISpn6cbd3Pb6VK/HvH33OAac0qqaIhLBVpn7t4yI1CEz3vy5ws3djpeTkYtp+r/s1pt9Ww+QmZpFt8GdueqxcfQ951T/kxBdiM64D3BRNgkBMMG1E53znyqJUwgROP06t+TSoT2Asn+Civ//kiE96N9ZummLikmxah2ybuEm8GN8K6FRnN/Jgie7N+7lv7e9x9oFG0sei0uO4ZrHL+WiO0ehlB8ZkeNX0N5aw7shbyo6+iGUEXlS8QohAkcpxcNXnMkpLRvx6Zzl7Dpo/btu1SiBa8/uzZjTu/n3N0HUS5KI1CFGBYWjFTn35rNP6jr7th3gntMfIz+7oMzjmUeyeeuej8hOz+HaJy71eR7t3IT1K+jyclQBuPeC0dHzeXQhuLYDGuztUcrzLsRCiMBQSjHm9G5cNKgrWXkOAGIjwyQBET7J1Ewd0m90L5/JSFRcJOPuPe+krvPJE9+Qn1NQblVOsc+f/Za0g743wVMqHL+GcFR4hQ9r7UTnvIE+fDr66EXoo2PQhwdhZv/XSk5EtcgrKGT/0Uxy8h3BDkXUAEop4qLCiYsKlyRE+EWKVeuQPZv3cXP3B3C73RXe35WCUwZ0xDQ1LTs349ybz6broMrt/ZKXnc+4pBu8dlU1DMX4F67h0n9c6PVc2rkFffR8L0cosLVBJf9c7g+a1iY6425wzKb8F6sg9AxUwjsoJXtcBMqe1Az+9/1iZq/Ygss0MZRiaI+23Hr+QDo2lxUSQtRnUqxaT7Xo1Ix/fXMfdrsNw3bsR6uUAgVaw6alW9m0dCu/frGAewf/i//e9l6lClczU7N8tnY3bIZfvUpUSEcIPQPPv4YaFX17xe+qHHPBMYuKR1Q0FM4rel4Ews6DaVzzwpfMWrG5pJW3qTUL1+7g+he/Yu3OA0GOUAhRW0iNSB0zeGx/Ptn2Jj++O5vV89ejlOLgzsOkHUzHdGvMopbsxcnED+/OJi+7gOYdmxCTGM3QSwaQ2DjB4/kjYyN8xuB2m8Q39G/JrYp/FZ1+OziXYv066pIPFf0PVETFoyo67xvARvnVNsUMdN7XqPDRfsVRG7jcJiu27iUjO59GidH0bNv0pIe+nS43izfu5khmLslxUQw8pZVfO6X+35e/kldQiPu4Fv9uU6O1yeMTf2HaUzfI0Hw9lp6dx49LN7InNZPYyDDO6dOJ9s2Sgx2WqIEkEamDGrZI5m/PXQnAxqVbuXvgo16P/+3LhdjsNkzT5J37JjLunnMZ/+9rsNnK35DWL9rs8/ra1Jx19RC/YlVGDCR+Cs7l6IIfwcwBlQAhbcBIRptpKKOCxmjunXhOQsBa/rvLrxhqgx+WbuC1KQtLNhUDaN4gjkeuOIuBXU6sN8OPSzfyyrfzycjJL3ksLiqcBy45g/MHdPH4upTD6azYutfj86bWpBzOYNX2/fRqf/LbCYja5+t5q3h18nzcWmMzFFprPvzlT84+rQPP3jCKsBC59Yhj5LehjlsxazWG3cD0MZ3idlk3dY3m2//8gGEY3PziteWOWzBlse/N7oDY5Bi/Y1RKQWhfsLdDZ/4LCr6HfF006WJHR4xFxf4LpUqNxhjx4E7Ba7FrDWmElpVbwPQ/1jFz+WZyCwpp1zSJS4f2pH/nln6NGMz4Yz1Pf1Z+mmnfkUzuemsab905lv6VbBT1y7JNPD7xl3KPZ+YW8MQnM1FKcV7/Uyp87a5DvguRAXYdTJNEpB6auXwzL34zt+Rzl/vYv9HfVm4j1D6b5/5Wd0YqxcmTGpE6znSbKH+6nJWmYcprP5KRmlnuqYKcAp9JCMCEq/9buUuaOeijV1m1H2WSCxfkT0Gn34LWx0ZAVPgFPs6oUBEXVSqGQNh5MI2Ln/6E/05byMaUw6QczmDBmh3c/vpUnvtiTslUmScOp4tXv51f4XNaW51tX/l2fqX253GbJq9OWeD1mNemLsDlYVVUZFiIX9eJDJNl1PWN1pr//bDY418cU2t+/nMT+46U/9si6i9JROq4UwZ0KBntqAy3282iaX+We7xF5+ZlCmE9Wfzdcrat2un/BfO/9TLdYkLh0qIkpUjEODCaYtWJHM8GRkOIuMT/6weA2zS5+63pZOTmUzpPKK6rmLZoHZMXeN7BGGDR+l1ke1kWqzVs23+UrfuO+B3XX1v3cSQz1+sxR7PyWLFlT4XP9WzblPho77VCoXYbg7pKO+/6JuVwBrsPpXtdlK+UYu6qbdUWk6j5JBGp43qP7EnjNg39Sh5KMwyDnIy8co+fe/NZHvuHlGazG8z/5g+/r6fzJvmKCJ0/peQzZUSjkj4He+fiK1KSlNjboxK/CPoeNYvW72LfkcxyBZ3FFPDZnBVeR0WOZOb6NZ6V6iOxKC0tq/zPtSJHsys+LsRu42Yf27lffdZpxERW3P9F1F15Dt/9ewyl/NqtV9QfkojUcYZh8PS0h4iKjahUMmK6TZq2a1Tu8SZtGnH1Yxf7PoFS5GT4f3PETMV7czMT3GWXhCpbM1TSVFTiN6joe1DRd6MSv0QlfYeyB39fixVb9mL30kpfA/uPZpGa6XmX4eS4KH9avtEgLsrvuBolRvt3XILnOp8rhp/K388bgGEoDKWw2wwMw5oEvPyMntx+4SC/4xF1R7OkOOw+/s64TJM2jf3bmVvUD1KsWg+07dGK99a8woy3ZvLr5wvILXpHnO+p3kNBTEI0Ay7oXeH5rn78Yia/8h2FBZ7f1ViJTGP/gzSSwZ2F52TEBrby57MKXXtZHzWMqTVuP3q0eCvvOL1ra2IiwjxOzygF7Zok0aESyyJ7tGlKs+Q49h/NrPDaCmicFEuvdp4LTZVS/P38gYwb3J0f/9zI4fQcEmMiGd2vM82Sa0aRcE2WmVvAT0s3sutQOlHhIZx9Wge6tKrEv5caKjYqnJG9OzFz+aYKRwKVgrjIcIb1bBeE6ERNJYlIPZHcLImb/u8qbvq/qwCrC+tdAx8lP7tsq3ZlKNBw33u3EhJacVFiSGgI5958Nt+9PdPjNI1hGJx93Rl+x6ciL0Vn/9vLEW5UxDirKNO5HFxbgHAIOwNlq5m9CaLDQ32OZsREhNEw3vMIRViInfsuGcozn80u95zVp07xwCVnVKpfh2EoHr58OPe8PQOULpOMFJ/mn5cPxzB8n7NBfDQ3jOzr97UFfLd4Pf/35a843W5shoHWmomzljOwSytevPl8osJrd5HvPeMG89fWvaRm5pRJRqwRM8WzfxvtV68aUX/I1Ew91aJTM95cMoH+551W5ibW4bS2TPjlMYaM81ED8K+Ladgyudx0T/G5bn3lehL8bGoGQMRlYGtNxcWnBoT0RRuN0UdGo9OuRmc9g856BJ06BDPz8Rq5t8zOg2k+jyme0vBmzKBuPH3dSBJjyhaINk2K4/U7x1R66S7A6d3a8PqdY2jZsGzzuhYN4nn99jEM7d620ucUvi1at5OnPp1FocuN1laTuuKb9dJNKTzy4U9BjvDkNYiL5rOHr2Tc4O4l/UIUMPCUVnz4wGWc3rV1UOMTNY/sNSNIP5TB4T1HiU2Mpknb8nUh3l734aNf8usXC3EVWjvotuzSjGsf6cLQC7MAGyp0IIT29+sdu3YfRWc9dtwSXhuEXwRR10PalaALgONHYQwIOwcjwfOSYa1NwAEEZiOunHwHSqky72b/9tI3rN6x3+vrlIKXb7mAYT3b+YzL6XazYste0rPzaZwYQ8+2Tf0atfBGa83GlEOkZubSIC6KU1o2km6oAXT9i1+xftchTC9/dr969Go6tWhYjVEFTqHTRXpOPlHhoURHhAU7HFGNKnP/lkREnLTczFwO7DxMeNg+miT+C6X3c6xduxvsHVEJ76Js/jW30q694FwFygYhfVG2ZMyMh6FgBl67qcY8hQofBoV/AiaE9ALs6Nz3IX86UAAqBiIuQ0WNR9mSTurr1lrz3eINfD5nBdsPWHvrdGzegGvP7s25/Trz0Ps/MHf1dp+9QgAuHtydR686S5IAIDUzh5z8QhrGR9f6aYrSjmblMuKf73k9xmYobhzVj9sukGJfUbtV5v4tNSLipEXFRdGueyT6yL2gi1fKuI4d4NqOTrsWkn5AGZE+z6fszcHevORzrV1Wt1WvLd2B7KfQ2cc/aMNKiIpGUXQ25E202sknTUIVFcBqXQD5U609bNz7wEhERYyDyCtQRny5S2mtmfDVb3y7cA2lc4dt+1J5fOIvbNmbynn9u/DrSv/6JUz5fS2ndWjO6H6dfR9cRy3duJt3vl/MmqIN80LsNs7t25nbLxpEgzj/VvrUZPl+LFlVsrRV1ENSIyKqhM77EnQOFScLbnDvtUYmTujk+cCJ/nF2U34qxw1mKjrrKev0RV1dddbT4NoEOgvcu9A5r6GPXIR2l59eWbxxN98uXGO9vtSAR/Hgx2dzVhAdHsqp7Zpi+DHKYSjFF7+uqPyXV0fMXL6Z29+YyrpdB0sec7rc/LB0A9e98JXXJc61RYP4aCI8FIAXc7lN2taRpa0ut0l2XoHHDr0i+DbtOcysFZv5Y8MuCp0u3y8IEBkREVUj/zvK3/CPk/sWpnkYFfsMSlWial5FgooqNdpSFdzgmIt2H0TnvAGujZRfOmyCeRidcR8q6Zsyz0yatxqboTw2K7MZiim/r+GNO8dy91vTWbltn9doTK3ZmHIY09QnXfdR2+Q7nDz7+WzQYB73M3CbmiNZubw1YxFPXXdOkCKsGmEhdsac3pVJ81dXvLQVCA8N4Zw+nao/uCq070gmH838kx+XbKTQ5SYsxM4FA7rwt1F9aZIoU+w1wYbdB3n28zls3pta8lhsZBi3nDeAK4f3qvYpYklERNXwN0nIn2ytcIm5H2VrgtZutGMZmHvB1ggVejpKHb8Sx4aOuATyPsfn9EzlgkYXriyqH/F0Xjc4V6KdG1EhxzaB27z3sMckBKwb6OY9qUSGhZCdV+BXNMpQBKtEZNfBNL6au4p5q7dR6HLTpWUjLhvWk6Hd2wb8j9Kcv7Z4nY5wm9b+JP+4dFitL3j8+3kDWbxhNympGWVqh4yiZfNPXz+SyFpcF7PjwFH+9vI35BUUlvz7cDhdTFu0ljkrtzLxwcvLrdQS1WvL3lRuemUyzuO2/sjKc/Dy5PnkO1zcNLpftcYkUzOiatjb4PevU8EMdOoZmKkXog/3hYzrIOtRSL8Jfag7ZtZL5V6iom6xmp5VNfMAfk37OFeW+dTXEDtY7273pGawbf9Rn8cqBf06tQhKseqi9bu4/LnPmPr7GlIzc8nMLWDp5hTue+c7XvxmbqU21DsRuw6l++zG6XSbHM7wPT2zdd8RJs1fzeT5q9lxwPf3vbrFRoUz8cEruGp4rzKFuH06tuDdey/h7NM6BjG6k/fkJzPLJCHF3KYmO6+AZz4v3w9HVK83ZyzC5XZ7XLn17g+LSc/Jr9aYZESkFtFmHhR8h86fAWY62FuhIi6HsGHlRhGqm4q8Cp250veBpbk3VfCgE/LexzT3Y8T/59j5bQ0gaRI67Xpw7zqpWI+dNBbs/v7hL/v9Pfu0jnz0y58e/zErpTj7tA5k53nesK40reH6EX38jKXqZOYW8OC73+MyzbK1LkU3km/mr6Znu6aM6hu4ItroiFCvy1mLeVtBczgjh0c//Im/tu1DcWySrX/nljz/t9Ekxvoukq4usVHh3H/JGdw1ZjDpOflEhIUQc9xIT2pmDlMXrmXh2p0Uutz0bNeES4f2pGPzBkGK2rcte1NZv/uQx+fdpuavrfvYdTCN1nWkDqa2Sc/OY9G6nV4bLbq1yczlm7li2KnVFZaMiNQW2n0IffQidNYT4PwL3DvAsQCdcSs64060DnKlffh5EDqs6s5X8CNm4boyDylbE1Tip1Tc9KzyVNR4VEgvUN53kgUgtOxyyrGDuxFiNyrckM4wFDERoYw9vRtNk2L92rTu/P5dTqgx2cn6fvF6HC6XxzbzhlJ8/utfJZ/n5DuY+vta3pj+O5/OXs6BtKyTjuGsXh28LnFWCrq0auRx75vcgkLGvzqppGdL6TMt37KHm/8zmYLC4BXieRJit9EwPrpcErJi617GPDGR939ayoaUQ2zbf4Tpi9ZxxfOf80Wpn0VN4+8O0Nv9GCEUgXE0K89nt2ebYZDqx+hjVZJEpJbQGfeAe2/xZ0X/LZrjc/yKznnb9zm0A60DM+SmlB2V8BZEXFV1J815o/x1bI0h5sETOJlR9FGUxERcA1G3oIwoiLwaPKYLNgg7s8wmesu37OHGl7/B4XRX+I9aAY9eeRYJMZEkxETSKNHz5nHF7hxzeuW+nCpSvFTWE1NrNu4+hNs0mb5oHSP++R7PfzGHz+as4PVpv3P+vz5kwle/ntTKiJYNE+jZtqnH57WG284f6PH5GX+sY19qxbscu03NzoNp/LKsotG3miczt4B73pqOw+kqM0pU/LW98u18lm3eE6zwvAoP9W+APczP40TVS4yJ9PnGyG3qSm2iWRUkEakFtHO9NQrisaBSQ95naF3xNIAu+BXz6BXoQ93Rh3pipo5C531d1G30BOJxH8DMfg3z6NWYadegc95Bu4+gVAhG3FNg706V/Gq5Ku7BoSL/BkYb/86hGkL8RIi8CsLPgcgbUMk/YcQ9UTKdpaLvhbAzi15QPNpSFL+9Myru2B44G3Yf5I7Xp5LqZWdhreGZz2ez62Aa+Q4nRzO9F/Iq4LeVW/37eqqYoaz9P7xRSvHbym088/lsHE4XGmtppqmtfWq+XbiGV76df8IxzF+z3WsH2qE92nJ6N88/7++XbPD6Lk8p65hg2JhyiDdnLOKlSXOZPH+1x80Li32/eD35hU6PU1U2Q/H5nJq5zLtf55aEhXgfrYwIC6F3h+ZejxGBkxgbycAurbyuzDOUYmTv6l25Ve9SU21mQ/4MtGsrqHBU+AgI6V2zO1oWLsG6MXpJHHQWuDZDSI+yD+e8h855mTKJgXunNcVTuBziXqxUfYkumInOuK8oFiseXbgcct6BhHdQYaej4l9CH73SiulkVrkYx7JyrQuhYA668A/rupEXQc5rPk6gIKw3RvggCPfcqVKpUIh/CwoXofMmWSNPRjIqYgyEj7CeL/LO94txa+21psHUGofTxbs/LuH2Cwfh9DFaYLMZ7D2SWe7x9Ow81uw4gAa6tW5McgDepfTv3JJZK7Z4fN4wFL07NOft7/8oU3tRmtYwef5qbhzVt9KNx7TWvDH9d5TyvAvxH+t3kZ6dR0pqBrsOphMZFsLALq1KVtCkZ3sf5dPa6mq6/2gmX/z6Fz8u3URugYOmSbFcMrQnlwzt4Vfxsb9fz8aUw2zbd4RvF65h3a6D2AyFUgq32+TVKQt4/JqzObffKRW+/s/NKV53Y3abmj83p1RJrFUtJiKMK4f34pNZyz0mhtee3ZuIsKr5XosTc+eYwSx/8Wtc2qzw79j40f2qvaaqXiUiOv8ndOY/gUKK3/nqvI8hpDckvI0yavmysuN+qbRzS1ESAmWTmKLjCr6DsOEQcZ5/p3dtK0pC3JS9JVn7uOj0W6HBbJS9LSRPR+d+AHmTAf+Wr5YTcfGx66bdVLTCpfgdlxvr19fb3L9GhfnXe0IpA8KGoMKGeDwmM7eAP9bv8jnHCtYNY85fW7hn7GDfx7pNtu07yt7UDJo3iCff4eTFSXP5YclG3Kb1czOUYmSfjjxyxZnERIb79TX5Y1Tfzrwx/Xcy8woqvAGapmZk7448/+WvXs+j0cxdtZ3LzuhZqevvPJjGjgPeNwd0uU0ue+4zjmbllTwWFmLjuhF9+Pt5A2maFMuRzFyPyaFhKBKiI7jiuc/JL3SWTHPsSc3ktakL+OXPTbx73yUnvTR49Y79PP/FnHKrpKzrHVvK+vjHv5AYE8mACmqC/FmgFOxNObLzHazbeRDTNOnSqhEJMcduWrdfeDrpOfnM+GM9tlLvut2m5pIhPbjl3AHBCFmU0rlFQ96771Ke+XxWmX97UeGhjB/dn+tG9K72mOpNIqILl6Ez7yv+jDI3MOcq6yaa+LXPkRGtTShcCkUjKoQNQ9kCvEFVSG98NgtTURBSdgWIzv8a68btaVTCQOd9gfI3Ecn7HOt7V+H7YsCJzvsaFXOvVVga+zg65l/ozEehYIpf1yihYlAR46yup2nXWauE4Livxdtoiw1sLSH87Mpd14usvAK/kpBi1g1I0a9zS5Zv3uPxRqmBZVtSuOiJj7no9G7sOpjGmh0Hyhxvas2sFVvYeSCNjx+8wu/5eF8WrtuBy22Wu7kVj1Dcd/FQ2jfzvWzaUIrcAv9WCJWWmetfkppWKgkBcDjdvP/TUvIKChk3uDurtnue2jFNzZ7UTPIKneWKYrWGzftSeXPGIh6+4kwPZ/Btw+6D3PKfb3H7USujlOK9H5dUmIic2q4Zf2zY7XHJtM1QnNrevz2bqprD6eL1aQuZsnAthUU9KGyGwei+nXjw8uHERIRhtxk8ee1IrjnrNH5YupEjmbk0jI/m/AFdaCMrZWqMHm2bMPnx61i/+xAph9OJCg+lf+dWVfZ3pbLqTyKS8w7W9ISHFuTOleBcDqF9PZ+jcDU6835w7+HYVImBjrgUFft4mSH8KhXSE+xdrfbjFcZvQOSVqONXfzjXezi+mAmuSsydF/zq+3yOeRBzb6mHUsHeAmwdwO1vHUQkKvFrlBGDzv0MzKN4Tn6KC1CdHPt1doG9HSrhgyr9mSTGRGK3GX4XZtoNg7iocG47fyDjt+yxpjY8ZDLFj09ftK7iA7BuqJv3pvLZ7OWkZuYya8VmCgpdtG6cyOVn9OSCgV199uMobf6a7TzyQcXbzmsNVw4/lWvP7s2RzFwMpbxOR7lNTcsGlR9R9LfTpqcrfzl3JdOeuoFe7Zuxevv+cjEaStGxeQM27Tns8dymqZnxx3ruHjP4hJuJvTZ1IW6z4qHuctfTmlXb95Oek09CdNl/s2NO78r7Py3B6aq4ENptaq4+s9cJxXgyTFPzwLvfs2TD7uOKaE1+WraJrfuO8NGDl5dMcbVrmszdYwaTkZMPShEfVXWjeKJqKKXo1rox3Vo3DnYo9aNYVet8KFyE95uoHV0w0/M5XNusjdvcxa26zWP/zZ9UNOUTGEopVPzrYDSg7OqOoh9f6ACIvBad+ylm1r/RuR+i3QfBn2JUPwtWtc4vSgh8OTbSpHM/RKcOhZzXwb0Tv3/d4p5HhXSwzuGY5eNgE1QkKvZ5iLgMIq9GJXyMSvquZEO7qhIVHsrI3p3KDDl7YjMUI3p3JCIshJ7tmvLabRcRF+XHMmEfFPC/HxYzbdFasvIcFLrcbN2XyrNfzOHed2bgdPtXk6O15r9TF1r/7+GYqb+vI7egkOS4KIb2aOvx61ZAXFQ4Q3u0rfTX0zgxhgGntPTre1rxtRVz/trKm3eN5ZKhPcoUS4aH2rnyzF4M69HO5/kdThe7DnmeIko5nM6CtTtYvmVPuY6UB9OyWb5lr1+7LJeW7ygs91hSbBQv3XI+NptRJubi/7/53P4M6V757/PJWrhuB3+s31VhomWami17U/lhsfWmRmvNtwvXMPapiZz10Luc9eD/GPvURKb+vjbgzfFE7VQ/RkR0Pp7/3JY+Ls/zUzlvYb3rrujGraHgR7Tz76iQwDR+UvYWkPw95H+Lzp8GZgbYWqIir0C79kPqWejiERpMyH4JDH+mjFxorctMSWn3PiiYabVtt7WB8BGQPw3v9RgABoRaTbl0/nR09r9LPefvCh07KqxUYamZh8+fnc6E0H4YkZf6eY0Td/uFA1m0fic5+Q6PLd4NQxEeGsIt5w2goNDFgbQs2jRJ5Jf/G8/X81bxWlECcCJKFm6Xunbx3/bFG3bx2ewV3DjKd3vmrfuOsOtQutdjHE4X89ds59x+p3D/xUNZtW0f2cd93cUb+j117UhC7CfW3+Uflw7jun9/hcPp8to2vyKGoTialUdEaAgPX3Emd1x0Opv3HEah6NyyIVHhoXw6e7lfdRV2W/n4dx5M4/kv5vBXqb2C4qMjuHl0f64YfipKKY5mVX4PpIiwEJJiKy4+HtK9LZMfv45v5q1i/prtOF0mPdo24Yrhp9KnY4tKX6sqFNd8ePv5TP19LZcM7cFzX8xh2qJ1Zd4y7TmcwXNfzGFTymEeufLMmr04QFS7+pGIqHhQCaC9/eF1o+ztK3xGawcU/IKvmgRd8H3AEhEAZcRB1E2oqJuOxZb3NeS+WuqoUjd88yC+udCOhajwoWhdiM58EgqmYr3PNQAXZMWCivPjXCYq4mq01tZGcifCaAi6VN1ASFdwrcNXMqLzvkDFPnpCl9RmTtGmdwrsXVCG54rxpklxfPLAEF78ciKLtzVFV7D09ZRmbh684jImL1jNtEXrSrZ/b5oUG9AOpVrDV3NX0qFZMoVON22aJNK2SVKFx2b40cLZUKrkuOYN4vn8kat4Y/rvzPlra8kNqUfbJtx+4aCTukG2bJjAE9eM4LNfV7B+l+fOnBUxTU2jhGMrdWIiwsrFMqhLa5/JX4O4KNo1Lfu92pOawQ0vfk3ucSMXGTn5vDR5Hll5Bfz9/IGVXmFgMxRjBnUjLMTzn99WjRJ46PLhPHT58EqdO1AOHM3ymoRo4GB6Nos37mZa0fSiPu55sJZ6n9WrfVCa94maq14kIkoZ6MirIPcdPL8zt0PE2Iqf0rn4tQzV9P4Os6pp7ULn/PfkT5Txd8ykqZA7EQqmc6wgteh7pbOKluL6YDRAhXRAu7YV1dGcAPMQ+uhlkDQNZUu2Rnzyv/b9OsdcoHKJiDbzrFVFeZOBokJLFYmOvBoVfQ/oQij4Ae1cgzVSMwTChtM84mNev/onDmZGkHI0DrvNJKcgBIcrhDbJ6TRNyGb8pwls3Z9Z5p34/qNZfPTLn5WKsbKOZuVxz9szSj7v0aYJT1w7olxC0tiP2gxT6zI1HE2T4phw03k8ckUBhzNyiI0Kp2F85Zbrlqa15pt5q/jwlz9LVsQYCvp0aklUWAjz12zH5wCJgtH9vCd37ZslM+CUVizbnOLxZnr9yD7YjLJTh//7fjF5jkKPUy7v/7yUcUO60yQxltM6NGPVtvI1KsezGYrmDeK55bzqWT3iNk12HEjD6XLRomFCuS6u/kqOi2LrviNev77EmEi/dqWetGC1JCKijHqRiIDVzls75hW98y2djBiARsU+63n5rooBIgDv7yKVrXx3SK1d4NoOuMHepnxB6clwrvSzbsMXNxy9jJKb8QlRx/Zt8TLF5Vcs5hF07geo2IdRIV3QKh50hu/XVYLWhej0m4o2syv1+6DzIPcDtONPa2UUeVjFsMpKiFQj0IcBTeO4HBrHlW+F/MH8Pmzdl1mpFTbFlLJWIrjNClay4NcEYxnrdh/khpe+4fOHr6Jlw/iSx1s2jKdnu6asPW51TmlxUeEMrqCRWGxUOLFVUHz45oxFfDxzWZnHTA0rtuzBbjN8JyHA+FH9/OpdMuGmc7njjals2H0Iw1CYpi65YV4x7FSuHF62ADSvoJDZK7Z4nyrS8NPSTVw/sg/3jB3C+FcmgYnH72dEWAhjBnXjlvMGEBfg4k2tNVMWruHDX/7kULr1Oxpit3Fe/1O4Z+yQSl///AFdWLR+l8fnlYKLBnXlm3mrfe5K7W8reFF/1J9ExIiCxM/Rue9C3ldWXQFASB9U9G2oMM8ttpUKQUeOg7yv8XzDM8uMqGhtQt7H6NwPwSz6h6ci0RGXW8tbqyIh0ZWfm/bsZJIQAI0q6vuBrSW+e3x447YKgGP+ac0lh51dNF3kaTTLVrTEuRLyvwenpw6VGlyry8ZT8pT3qQOt4dvlXdBYS3e9MRRlbraGoTBQ/Pvm8/hl2WZ+/WtryU1NAd3aNGbtTn+m244xTU2+o5D3f1rCszeMKvPcg5cO46ZXvsHpKrvaozjqR64884TrPnxJOZxRLgkp5jY1btN3YhkWYuPvXlq/lxYXFc4nD13B7+t2MnPZZjLzCmieHM/Ywd3o3KJ8LVV6Tj4u03tdk2EoDmdkA9C9TRPeuedinv1iDrtL1d7ERIRx/Yg+jOrbieS4KEI9TMfsP5rFN/NWMWv5ZvILnbRtksRlZ/RkZO9OXrtgevLWd3+UG31zutx8v3g9q7fvZ+JDV1RqdOTMXu3p2roRG1MOlxshshmKxgkxjB3cne8X+16FFxUWoNWFotaqN4kIWMmIirkfHX23NY2iwlGG731AAFTUbeiC2UUjEBX8kYy6HWWz1vdrra3OpfmTyh6j8yDvE7RzNSR+evJLS22tT+71VcaAkO4QPhIAZcSjw8+Fgh854c6qOscqMlaRqKhr0AXfejnYjYq8pnKnz/+aExtj8M7hsnEkx78OqC0bJrL3SAYut4nC6nJ66/kD6d6mCcN7tudQejZ/bd2HRtOzbVOaJMZy+XOfsetQWqWKOt2mZubyzTx61VllOoh2adWIDx+4nJcnzyvTh6N140TuHjuYM3q08/salfXd4vU+lwT70rpxYqWKHm2GwRk92vn1dcVGhfuMz9S6TDOv0zo0Z+qT17N250H2HskgNjKcfp1aeEw+iq3ZcYDbX59Splh3zY4DrNq+n99WbWPCTeeWmzbyZtfBNI9TgG5Ts/tQOl/8+he3+pnEAYTYbLx91zie/mw2c1dtK/Ovplf75jz3t1HERIQxsk9H3v1hidddqUf09nfH6/pj057DzFy+may8Alokx3PegFMq3aW4NquWROTtt9/mpZde4sCBA3Tt2pXXXnuNIUM8d7AMNKXsYKvcdtrK1hCSJqOzngXHb5S8OzeSUVG3F22cVsS5snwSUsK09o3JnwKRV55Q/CUx2VujQ/p62YfGR1v4KmFA+GhraqtUYqVi/ol2LAd9kPJTYcW8xRZuNYwDVEgXiHkMnf08ZRu0Wf+vYv6JCj3W0VO7Dxf1XAmB0F4oVcEwtHsvVZ2EAITa3diUG7f2PZLQsmE8nz18JUez8oiNDCP+uJ4SjRJiytU/vH7nGP7+2rfsTc0saTrmTzrlcptk5RaUa2XepVUjPvrH5aQczuBgWhbx0RF0aJYc0FUN6Tn5TF+07qSSEID9R05+519PYiLCGNqjLQvX7vCY9GlTc26pn4/WmiNZuSTHRXFKq4aEVLAK53iFThf3vTODgkJXuQZ2AL/+tZWv2q7imrNO8zv2GYu9r3AxtebbBWsqlYgAxESG8/LfL2D/0cyS5co92jYpU380bnB3vvj1L3IKytfWWLtShzH29G6Vum5dVlDo4rGPfmLu6u3YDGs3b1Nr3vxuEfeOHcI1Z1d/l9NgCHgi8s0333Dvvffy9ttvc/rpp/Puu+8yevRoNmzYQMuWLX2foAZRtiaohLfR7kPg2mltHx/S1UpsStF5k/De0VSh875EnWQiAqDinraKO3X+cdczsH685XsVVBmViEqeXnG/DjMdlLuCPiU2iLoVcr2tqrFBxNiSPXC0dkDYGWA0ttrSFy4BNIT2R0XegArrbx3nTkVnPQOO2ZQkOSoKHfk3VPQdKFXqxqASgKrfjtxQMKKn5pdVvo5TxESGERUeSlQlmmg1SYxl8uPXMeevrfy6ciu5BYXkO5xs2H3I643dZihivbSGb9kwvkwNSaC43Ca3/3cK6dknU0dkKV6NFCi3nj+QxRt2oT3syXH5sFNpmmStJpu5bDMf/rK0pL17XFQ4l53RkxtH9fO6OubXldtI97KCSQNf/voXVw3v5fcUzb4jmT6TvLTsPJwu9wlNvTVNiuPCgRWvokuKjeLdey/h7remk5qZWzKS4zZNkmOjeP2OMWVGkeq7Zz6fxfw1OwBKtnMAQMOrUxaQEBPJef0r3peoLgl4IvLqq69y0003MX78eABee+01Zs6cyTvvvMOECRMCffmAULZGYGvk+QD3LrxPSegTX1VyfCz29pD0LTr7NXDMwroBKwgdjIq519obxr27smfF93tsBfZWFSYh2sxAp18HZvmN3MANeR9D6DAoXED5URFb0XTMeKu9e87rkD/5WD2MvRMq7v/A1hxdMBvt+BXce9Chp0P6NeDeX/acOhdy30S7D6Dij/2+qchx6OyX/Pg6K+/6ofnMWh3rc1j/nD6V3+EyIyefdbsOEhsZxuPXjCAhOoK9qRlc9MTHHl9jMxRnn9axSjcby84r4MelG9m0J5VQu40h3dswqGtrn1MI89dsZ/PeVJ/nL77levoOKqBJkn9dWU9Ux+YNeO++S3nik5ll6j5C7TauObs3t11gjSh8+POfvPXdIkoPImXmFvDhz3/y19Z9vHXXWI/TM6t37PfZrfdgejZp2Xl+b3oYExmGoRRuL79/oXZbpbrwVkanFg354fmbmLtqGyu27EMp6N2hOcNObefXKFF9sTc1g5nLNnv9HX/vxyWc269zne+7EtBEpLCwkBUrVvDwww+XeXzkyJH88ccf5Y53OBw4HMeKJrOyAjf0GlBGPD6nRZR/tSn+UPa2qITX0WaW1VLdSEAZRfs6RF6Nzp6A7xuuDWzNIfpByH0dXJ53ZLVocG3CzHrGGpWwlxrdyp9StJS5omuaVv2HcwOE9CmaVipd1BoKRqIVs3MTmMclFq7N6Iy7ij4xsBq4uYCQovN4+DoLpqCdV6FCulufR1wGuZ+DeYjySePJTWl1SPicp698gse/rPiGaxiKrq0aMbCL/0sY8x1OXp48j++XbCi5adltBuf2O4UHLxvG5cNO5et5qyq8VliInb9X4XLRuau28dhHP+NwuorepSu+XbiGtk0SefPOcTRO9Py7PXP5Zr9qQ0LtdhwuL8XOCi4d2sPz81Wke5smTH3yelZt38+ug2lEhoUyqFvrkkLP3YfSeeu7RUD59v2m1vy1bS9TFq7lSg9t2f3tKFuZzrOj+nRm2u+etwqwGYrRAb65hdhsjOzdqdq3k69N5q3ZcWxTpwporF42Ow+meewHVFcEtMX7kSNHcLvdNGpUdvSgUaNGHDxYvvp/woQJxMXFlXy0aBGcLoInS4Wfj/cbmQ0iLqr66xqxKHu7Y0kIWHUoIafi/UetrOQl4V2MiJGopO8h5gWsm7sXOh/yvkIfvRBduOrYwwU/47sb6mFw/gm29hD9QNFUCUC+NYLj+BXMvXj/PpocS2KcPq5pQ+cf23hPGbGopC+tIlvrEUreh4f2A6O19/i9Mhh9yhQ+/sflJXtsKHWsC2n/zi15446xfhcgOt1u7nhjKjP+WF/mnbPLbfLDkg3c/voU7h4zmJvP7V9u06r2TZP58IHLaF1FG45t2H2QB9//AYfThaZ4hYsV0+5D6dz2+hSvbeYzcwv8qg354IFLuWLYqRU+ZxiKU1o24uJqSETAKrDs1b4ZYwd355y+ncqsNpn6+1rvSYKGb+av8vh0/1NaeR0NUQraNUkqVz/kTZ+OzenTsXnJ71tphlKE2u1cP6KP3+cTgVHgcFb4Myp3XGFgpyBrgmopVj0+8z6+pXixRx55hPvvv7/k86ysrNqZjISPhNyOx/qHlGEDFV3pVR4nSqkwSJxotajP+/y4Hh/K2r8m8hpU5BUoI956uHAhZPvbHMwNugCdcQc0mIdSIUVt2f19+VbI/cAaJSmjqqdM3OA+UOYRZWuKSpqEdq6HwhWAgrCBKHt7zIyHoCCFExsZMcG1nh5tk5n94t9ZvGE3G1MOEWK3MbhrG792sy3t17+2etxd1tSatTsPMvuvLdx2wSCuG9GHpZtSyHMU0rZxEl1aeZlCPAGfzFphFdRV8FzxiowFa3ZwVq8OFb6+VcME/tq61+uqn5YN4+naujFdWjWiRYN4Js5aRmqmNTUXHmpnzKBu3HnR6eUKb4Nh10HvK5iK39V6+pt3etfWtGgQz/6jmRWeR2ur2VplRi+UUvzntot4fOIvzFu9HUMplLJ+Pg3jo/n3zedVWWIqTlzbpkll60IqYLcZNE+Or56AgiigiUhycjI2m63c6Mfhw4fLjZIAhIWFERZ2Yp3/ahKlQiHhE2sKwbkcq3AVwA225qj4N6t8Qzbv8USAvR1a51F22kFbUzmFiyHqRusR7UJnPsKx7qr+MK3zOH6D8HMgpHPRJnf+LN11H+vpElA2MCoe3lQhXa1W8qUZMfjqA+Kbgc0wGNytTYWNwfw1bdE6r9MZCvh45jJm/7WFwxm5NEqI5qJB3ejUouzKsI0ph/hhyQaOZOaSHBfNBQO7VNhDwxOtNXNXb/N647UZirmrtpUkIlpr/tq2j00phwmx2xjUtTXfLlzj8fUKuOwMawWUUoorz+zFZcN6svNAGk63m9aNEqu01uVkRYaH+pxqCg8J8ZhI2AyDN+4cwy3/+ZbDGTkl1VnFq16uH9nnhIoVo8JDefXWC9l9KJ2Fa3fgcLrp3KIBA7q0qtRSYBE4Q7q3ISkmkrScvApnZ2yG4pw+naqkeWBNF9BEJDQ0lN69ezN79mzGjj3W7Gv27NlcdFHVT03UJMqWhEr6Eu3cAI6FgNuaIgkdWO2FR9p9FJ35WNFnx2fg2lqFkvcZRN1kxWr6LiYsz452rkWFn4OKvBJd8MNJRl3V3Khw/3/nVPgodN5nJ3gtA0J6lVtN5YnD6eLXlVvZXFT4ObRHW5wuN1/PW8XKbftIz8n3eqPTwK5D6aQczsDUmm37jrBw7U76dGzOf+8YY23h/r/v+HPzHmyGddM0lOKruSs5f8ApPHHNSL8KF92m9jqNAFYDtYJCa7ps674j/PP9H9h1KB1DKavJm4bmDeLYm1px8tkgPooh3cruLmszjEqPIlWXs3q1Z+byzR6ftxmKkT76ZrRsmMDUJ6/npz83MnuFtRKqQ/NkLhnSg64nuUV7q0YJtGpUP5aA1jYhNhvP3Tiau9+chql1mQTfZigaJcRw77jgtbmoTgGfmrn//vu59tpr6dOnDwMHDuS9994jJSWFW2+9NdCXrhFUSBcI6RLcIPKn4H2KQaNzP4PIG8GdwokVa2qKa0pUaF905A2QN/EEgg0EBSEDrdoPf4X0KSqmXYnnkR1P3ycTFTXer8ss3bibf37wI1l5Duw2A601HxY1ozq+86ovxclK8X//2rqPm1+dzNZ9R0q2ri/+Y1e8ouLHpRtJjo3i7rG+/+DZbQYtG8az53CG50p/pWjfLJn9RzO56ZVJJVvdl06k9h/JomWDeHIKHKRlH1u6qoAjmXmMfWoi9108lKsr0TsjWM7o2Y62TRLZfSi93EiRwkqirh3hOxGIDA/lkqE9uWRoT5/Hirqjf+eWfPzgFXzw81Lmr9mO1hAZFsJFg7oxfnS/erPUOeCJyOWXX87Ro0d55plnOHDgAN26deOnn36iVau6u+mR1vlgZoERb9VoBDse1ybfB5n7AUfRlMSJ1EW4UWFDSz5TMY+gVZy1AicAS2QrR4NrlbUUOPrOsv1EPFBKQcL/rOm1wsUU7zcDLlDREHUP5L5ZtBlg8ddX1GAt+l5U+Fk+r7Flbyp3vzW9pJX48aMNlUlCKmJqzYbdvlvSfzV3FTeO6ke0Hy2/rxh2Ki9Nmuf1mDGDuvHJ7OXkOwornMYxtSYlNYNTWjYkI+dY8arGmsoBeOXb+TRKiObs02p2F84Qm4237x7HtS98VVLHUkxjjZi0qYZ6jIJCFxk5+URHhPr1cxQ1R5dWjXj11gvJdzjJcxQSGxVe75Y5V0ux6u23387tt99eHZcKKu3ahs5+Exwzsd5Fh6LDL0DF3FnS/j0oVDi+6x0UYIewM7FGNipTqW2DkG5Fq3OKFC4p2u24upMQBYRh7Z1T6to6D3LfRrv3oaNuttrPmxnWRoURY6zOucefyYhFJX6Cdq5DF8yxepKYueDaAflfWJv82RqDK8W6Xkh3VORVVs2JHz6ZtRxTa0+r96qNw+lixZa9nNHTd+vzi4f0YMHaHSzdlFIm7uKN5O67eAjz12xnysI1XmtJDKXYmHLY4/NKWT0UzurVocxUptaapZtSmDR/NVv3pRIRGsKI3h0ZN7g7SbH+9dmoar+t3FYuCSn287LNtGmSxPjR/QNy7cMZObz742J+XLKRwqJRr9M6NOPCAV04o2f7gG+uJ6pORFhIjap/qk5K62D/GfQsKyuLuLg4MjMziY0NbPOik6Wd69BpV1tbx5cZyreBikUlfYOytw5ObAW/oTO8TYXZIGwoRsK7AJjZ/ylKIjwpLqkrmpqwt0clfIIqapuvdSH68JCiHXNr6q9X8QiHNQqhou9DRf/d49HaTEenXV/UOv64rz+kHyrx/UptZGiamgF3v+6z5qK6vDD+XL97Pjhdbr78bSVfz1tZsrNr7w7NGdS1NR/+8id5BVXXzfen58eX9CXRWvPC178xecGaMi3MDaWIjgjlf/deUqni26rgdLkZ+fB7ZOYWeDwmIjSEOS/+vcpvMgfSsrju31+RkZNfYdJnMxSj+nbmvnFDOZiexdxV2ylwumjfNJmRfTrWiFVHou6qzP27Xm16Fyhaa3TGQ6AdlJ/WcIPOQmc9gUr8NBjhWe3Rbe29rGTRqKibSz5T0fegcULuRxTV8Be9Lgyi/mZNR7h2WAlWxLkQdra1bLdYwWzQ6dRsZb8POucVq5dK5GUVHq0zHwHX1uLPiv5b9LN2LkdnvYCKe9r/q5tmjUlCADo283/vpRC7jetH9uG6Eb3JyXdgt9s4mpnLpc9+WvKu3JfiQQ5fb4MczmNNzWb8sZ7JC6wVN6VvvKbW5OQXcteb0/jp+fEB2zG4In9t3es1CQHIL3SyeONuzjy1fZVe+6VJ8zwmIWB9j35Ztok5f23B4XRjMxRKKVxuk5cnz+O5v40K6MaGQvhL1nFVBedKcG/Dc22FGwqXoF0p1RlVCaVsqMSPwF68GsHg2FSNHRX3Miq0T6njDYyYh1ANFqJiHoOov6Nin0M1/AMj5j6M2CdR8a+hQnuhHQvR2S+gHUtL5vetmpRA5LiB/XXVOW+hdfkbqXalgGMunotWTcifgjYz/L5WiN1Gw/jK766pOHYTrwo2Q9GnY/MT6iuhlCImMpyI0BC+mb8al9v0e5pJa99JSERYCI0Sjo2GfDp7uccJRlNrjmbl8evKrR6OCIwcP0d/cvMdvg+qhNTMHBas8bwhXzG3qXE4jxUqFye/eQWF/OPd71m784C3lwtRLSQRqQqubX4etz2wcXihbI0h/h2wdcBKmI79AdOFf6F1+T+oytYAFXUdRsw9qMhLUYZ149T5U9GHB6Oz/w35063uqunXotMuR5tpRbvd+nFHMipZNxMyCBI/A3snjvVmOZ7/G8iVYx4A18byjxcux/fXUwhOz/0xKnLZGT396qxYWnFL9aqggLioCJ64ZsRJn+vXlVt93hRLu3hId2IiwzwmVYahGDOoW0mn2Ow8B7sOpXvvnWsY/LV1XyWiPnmtGib4Pgho2ci/4/xVvFT7RBW/8qOiFVpCBJMkIlVB+bnEys8aAu1KQed+is79EO1YhC63g23lafcRSLsS3DuOe8YF+V+iM+7Fn3Ih7VhU1PCsuKW6i5I268616PTb0KHD8N7MTIGtLcT9X6W+BhU9HiO0PyrxCwgfQ5lRFxWLir4HIq/Bc5LiB122K6x2H7R2W/bvxeUfcW7GzP4PZtZT6NyP0GZayXNXntmLzi0b+p2MGErxr6vP9vlzCrEZPHjpGZzX/xSvKcv5A7rw1aNX07xBvF/X96bQ6WVfmFLioyO4Z+wQHrniLCbcdC42wyjXIt1QirZNkrj1gsptUx+MbcHaN0uma6tGHnfGNZSiTeNEerRpUqXXjayCehO3qVmwdkeZ6S8hgkFqRKpC2BCsd+JehmlVHIR674ugzVzrJu+YWfwiwARbC4h/7diGbSdA530M5lE89b3AMQecKyDU+x4UOud/eN6d1w3OlSjyrd1wC5fgsSYl+g5U2EDMqFsg9z0/voJQa2UORatZ4iegzX8W1W3YIaSr1dHWfRhdML1o51//6hWOMcDW2oqw4Ddrua9rg5+vtZfatwa0dlh1Q46fKS6M1bgh+2WIeQQVdS0RoSG8d+8lvP/zUibNW0V+oecbgs1QTHzwCrq2bsziDbuZ89dWj++In7xuJOf2OwWX2yQxJpKv560q6SMC0K5pEs/dMIpOVVjY2bllI5Zu3O1xVMRQMLh7W166+fySGo5BXVoz8cEr+HjmMuau3oZpauKjI7h0aA+uG9GHqPBjo1sxkWG0bpzA7oOeR0VcpslpHap/ddrj14zgxpe/weF0lWtKZTMMnrx2ZJU3MezUvCGNEqJLioVPlNbgKHQR5mF3YCGqg6yaqSJm1r8hr7i4szwV8zCqqI16RbTW6PS/Fd28j08WDFDhqKQZKPuJ9V8xDw0AneblCBtEjMOIe95zjGYO+rCvJlN2a++a6DvR6bcU7a5rp3RbeRX9ACr6FiuuzMchfzI+e5dEXIUR95SPaxddwbULnfEAuNb6dbzFBmHDMRLeRudNRmc9hv+N3QwIH4cRf2yEx8z4BxT84PH1Ku41q9C3SKHTxdRF63hl8jy0PtYAzNqkzMZ/bruQ/qdYP/v8QiePfvgT89fsKGnXrbUGBfeMHcK1Z5dtoJWVW8CSTbspKHTRrom1/0xV3xgXrt3BPW/P8HrMF49cxSktK977xul24yh0ERkW6nF0YfqidTzz+ewKnzMMRUJ0xEkXq6YczmBPagaL1+9iQ8ohtNb0P6UV14/o43XVy44DR3lzxiIWrNmBqTVKweCubbj9wkFVmvCVNv2PdTzzWcXfD3/FRYXz64u3evyeC3GiKnP/lkSkimjtQmc9DfnfUHZpqIaoW1DR93v9468L/0Sn+dgIz94ZlfgVyqhcvwStNfpQZ3zWOYQOw0j0PDqh3UfQqYN8XM0OEZdgxD2DabqhYAo45gMK7B1QkZeU6alipt9m7bTrS8O/MIzKFXeajmWQfgO+e6LYwEhEJU0GFYk+fLofr4GSkaGQU1EJHx2roXGloI+MwPP3W4GtDSr553K/EwfSspi6cC1/bduHoRT9T2nJmEHdSI4r/zPfsPsQM5dvJiffQbPkOC4Y0IUGJ1AAWxVKL60tvbN58T4st18wiPHnnlwvDa01//5mLpPmr67y5bsbUw7x4qR5rPawuaChFE9fN5LzBnjvkpyVW8DR7DwSoiMqtWPuiZo4axlvTP/9hHrRGEpx46h+3H6hr3/TQlSeLN8NAqXsqLhn0VE3ovO/A/OoVSAaMcZqmuWDzv+eY8tkPXBtQh+9FpI+Rxn+t/5VSqGNxKKpGU9sUEFTrzKMBFDxRf1BPHGj7B3QeZMh562ijq0AdquW5vg6GVsTfH7dJJRLQrSZBwW/oN0pKCMGwkeX+z4rc7+1DNmX8NGomIdQtsbo3E8pqXnxSIFqAPZmqIjLIeJ8a1qomGMOnqevsB537wD3LrCX3QyvSWIsd1x0uu+YsToynsjuuht2H2L9roPYbAb9O7ekWXJcpc9xPKUUD19xJt3bNuHLX1eyaY/VrKx72yZcP6IPw/xolubPNf55+XCG92zHpAWr2br3CBFhIYw4zWpolhh7Yu2wN6Yc4saXJ3mtlTC15vFPZtIkMZbTOjb3eFxsVHi1blJ2w8i+nN+/C9MXreP7JRvYk5qBUlaS4TY1MZFhZOc5ym3MZxiKdk2SuH6k96lYIaqDJCJVTNnboGLuqfwLzUz8mgZwr7f2cImuZKfaiEuLajE8LzFWEeO8nkIpGzryKsj9n4fzKCAUbaYWHVOaCwq+Rzv/gqTJKMNaRaAiLkbnfe7lqgZEXVHmEZ3/HTrriaLCUjsaE7L/jQ4fB7GPYxSPGLkP4TvJARV5dcluyNq9s+g13pIRjUqejLJ5KEAst8uxp9PkeX++iu1JzeCRD38q0/ZdAWf16sAT14446dbgSinO79+F8/t3welyo5TyazO9yl6j/ymtSqapqsJLk+bhdPtXT/TqlAV8/shVVXbtqpAcF8X4c/sz/tz+7DuSyYI1VgFqh2bJDOjSit/X7uT9n5eW/NyjwkMZe3o3bjlvQJk6HCGCRRKRmsLWDOvm5esPorZWX4T0RYX2LPtO3AsVdQM6fwaYhyu4hoKwURDSy4/z3Ix2LCgq4ix9ozUADTEPQfZzHl7tBvc+dO77qJiHrPOFdLUSiIJplB9BsIHRCBV1fckjumAuOvPBUseWShgKpkLBVMzQIajoW8FIruBrrYBRamdXFVNBHBVQXqbH7O3wPapiB5vnd9ZVLS0rjxtf/oaMnPwyj2vgt9XbOJKVy/v3X1plW8RXZ1OximTmFvD94vVs2ZtKSIiNod3bMrhbm5Kv72BaNpv3HCYjN59VHqZjKrIh5RB5BYVE1tAbeLPkOK48s+y/4zN6tuOMnu04kpmLw+miQVwUoVKcKmoQ+W2sIVTkJei8D/07WGdB+tVoFQ/Rf4fIG30WHyojEZK+QWc+CoW/l3omDCKvRsU84FcBozKiIPFzdO4HkPcJ6OyiZ4qSkrzPfJzBDXnfoKP/gVLWTUHFPY+2NYG8j0uNEigIOwMV+7QVe/GXnvOazxgpXIRO+x1in8P7aibD2h+mVAGwCh+FLjeac9xrQgegDC9znmFngUrw0uLeBuHnoYyTnxLx11dzV5KenV/hShvT1Kzavp/f1+2sE502Z63YzBMTZ+J0u4uWRium/b6Otk0SefaGUbz74xIWrtlxwpsP5Bc6a2wi4k1FdUZC1ASSiNQQyt7O2owt933/X6QzrKZi7sOo2Ed8X8PWGJX4kdUp1LUBCIHQflaNRWViNSIhpAta51CuFsK9G58jCjobdA4o62aulA0Vc4/19TtXWvv1hHQqV/NhxV1Bw7FyipKirKetRC3njYq+CkChYh4s+2hIF3TYmeCYR/mpFStRU9F3eL26UqEQ/xI6/Vas78Vxew8ZjUpGhKrLd0vWe22AZRiKn5ZurPWJyOod+3n0w5+tAm3ArTXFv4+7DqZx7QtfnVQjsFC7jbiowBehClGfSEOzGkRF/wMiTmD+Oe9jtGun/9ext0SFj0KFn1XpJASKNrXLfKz4s+Of9eMM9gqbuykjEhV2Oip8eMUFviWjL/5yAtFWm3p13GoSW1NUwgeo0H7l44h7FcLOLj6QknxdRaHiX0eF9vV5ZRU2FJX4JYSezrFWWxEQeTkq6duSDQKrS5aP/VBMU3M0u3prVgJh4sxl1qqdCp4zSy2LPlHnDzilyutehKjvZESkBlFKQewTaOc6cK3Dvx4WADZ0/rfl3t0HjGPeSWxqZ4PwUWU3yfP7pU3xv7cH1rHurai4/4PIy8Hxu1UUbGsOoX1LpoaOp4xIVMKbaNc2KJiF1rkoeztrdU0ldthVoaeiEj9Am9mgc60lwn7W9FS1hvEx7EnN8Pi8zVA0TarZS+R9cZsmC9ftxKxEq/nKSCjqCiuEqFqS2tcwShmoxPdLFY768yPS4K7GzavcezixXx0DsKOibj2hyyojAcLOwf8W7gqUtRJEqXAI6QE4wbUeChdWuMFdmVfb26Oib8eIeRAVMa5SSUjZuGOsabEgJSFg7e3irQTIbWrGDOpWfQEFgNttBiQJUQoGnNKSaU/dQExk9S3NFaK+kBGRGkgZCZD4JThXoQtmWct1fe3dUqqgM+BULJUalUABbjAaoOL/gwrpeBKXfgh9dKmfLdxdqLCzramkrOeLms1pSprNGY0h7iVU2Mk12qoNLhnSgx+WbGDnwbRybdgVMLJPJ3q1r/726FUpNMROs+Q49h/JPOFC1A7NkhkzqBsR4SEkx0QSHhpCtzaNCQ89+b1dhBAVk86qtYCZ8WjR8lbPN16V9C0qpEe1xKPNNPThwXhdomo0goTPUYVzQRdYO+aGDUWpk1/Wqd370NkvQ8HPeE6IbFYn2qQp6MyHoWAG5SsHDMCGSvoGFVK7RwP8kZVbwEuT5/HLss24Tev7FhUeypXDe3HLeQPqRO3DF7/+xatT5p9Qp1GAx646i4uHVM+/IyHqMmnxXsdo12700bGg86m4B8gIjIQ3qzUm88jFPvZyiUQ1+rPcdIQ2cyB/Otq5DFCo0P4QfkFJe/TK0GYGOu+rolUxbo6NvrjA3gWV8IG1sujIuV7OYoOwMzASvC3ZrVvSs/PYsu8IdsOgS+tGRNShd/tOl5u735rOn5tTyiQjpdvOexIZFsKCV2/HqKJeKkLUZ5KI1EHauQGdcb/VGrxkyawBERdbfUTMg9bKkJBu5UYdtM6H/B/QjoVAoTVyEnEJyldLd0+xaAf6UH/A+yoLFfcqKuL8Y68rXIZO/7tVuFmyksQEFVO0gsV3Q7UK4zHTIH8a2rkFVAQqfASEDkQpAzP7v0VdXr1PbamGK04oGQomrTV/bd3HvDXbcRS6aN8smdH9OhNzkh1Sazuny81Xc1fy9dxVHEy3Vlr179ySTi0a8OnsFR5f9987xjCkWxuPz1e1DbsPMXn+atbuOkio3cawnu0YN7i79PsQdYIkInWU1hqcK8C1GQhD21tZ+7kU/nHsIKMJKuaeknbt2rnF2tXXTKVMAoMNFfciKuK8ysfh2oU+MtLHUXaI+htG0Uoe7d6PTh0NOKh4d+EIVPLME06OPDGznoa8b/DV6VQ1mOfXnkA1RXp2Hve+M4O1Ow9iMwyUsoo1w0LsPHvDKM46rUOwQww6rTV5DichNqOkk+hPf27ktakLOZKZW3Jcs+RYHrvqbAZUYdt4Xz6e+SdvTF9UbvO+8FA7b901jp7tas/vYm3hNk1WbtvH4YwckmIi6d2xRZ2YjqypZNO7OkopBaF9ILQP2rULjl5cfr8S84BVE2HmQMTF6PQbwCxealucc5qAic58AOwtUSHd/Y5Bm7lo125/jsTqalr0Wd6XWB1OK6rpMK1pp/xvIPouv2Pxh7I1t/ai8Sq0eot9T5Jpau5+a3rJxnLF9R4ADqeLf37wIx8+cFm9v5kppcrtpXJuv1MY2bsTK7ftIz0nj8YJsXRv09ivrsJVZdG6nbwxfRFAmcJhU2sKCl3c/dZ0fnz+ppPe+0ccM3fVNl6cNJdD6TkljyXHRfHAxWdwTt9OQYxMgCQitZbOfqUoCal4ykFnv2g9Zx7xchaFzv0YFf+q7+uZ2eicVyBvKuC9OZbFDfnTMPO/hZAu4NzgMVaLiS6YjariRITwiyD7ZS8H2KwdklXtWZa5bHMK60ttXFeaBgwFH838k//ePqZa46ot7DaDvp1aBOTcLrfJ7BVbmLJwDXtSM4iNDOe8Aacw7vTuJbvyfjpnRbndcIuZWpOT7+DHpRu5fNipAYmxvpm/ejv/ePf7co8fyczlkY9+QqMZ1bdzECITxWRcqhbSZiY4ZuP9xu6E/Ckcq8WoiBscv/lxvVx02lVFUxz+JCFFzP1gHgLHAuu/Pi/k8P/cflK2ZFTMAx6etYGRgIq+s8qvG0i/rdrmdXM6t6n5fd1OCr1say+qXqHTxd1vTeOxj39m1fb9pGbmsv3AUd6YvojLnvuMvakZRXU9e713eFWwbPOe6gu8DjNNzUuT5wGeez6/PHk+Lre/7QhEIEgiUhuZqfju42EDMw/f+744fV8v7zNwbcVnLxOPv07+bLFuK2o4dlx4WqMLl2Fm/xcz+z9oxzyfjcjKRRY1HhX7f2A0KfWoAWHDUUmTUbbGlTpfsOU7nPj6uWoNDlflvk/i5Lz74xKWbrISiNKJhtaao1m5/OO9H6zfZ19Vedq/jRKEb2t3HmD/0Syv38+07DxJ/IJMpmaqiDZzoeAHtHM9qBBU2BkQOthjG/GTouL9OMgEewso3I/npMWwpk180HlfeTkHgAJbKzCSwbncj9gq4kZFlt1nR7sPoNNvK9qgz4Y1leSyWrTHv40K8X84VUVeAhHjrE3zdB7YWlf7fi9VpW2TJHw1EE2KiSS6Fu4QW1sVFLqYNH81nmr/3aZmy95U1u48SI+2TViz44DnUREFvep5fU9VKV2U7P24HN8HiYCREZEqoB0L0amD0VmPQ/63kPcVOn08+uiF6AC0Xle2ZAgdgM8fX/T9Ps5koiKv83qE1iaYvr4GDfZ2YG+Ff+3XjfL/H3VHmeW7Wuej064tWiEE1qhK0VSD+wA67Vq0+7Af1zpGKQMV0hUV2rfWJiEAFwzsgs1LcaWhFJcN61mtBZi+uE2TpZtS+H7xev7YsAunu26N1uw4cJTcgkKvxxiGYuW2fVxzdm+PSYhSEBZi58KBXQMRZr2THO/fUugG8bVr6X5dI4nISdLOTUW9MYpXr7gouWG6tqPTrkdr73+gToSKvp9jDbwqEHkdRmhPVOzzRceUThCKfuzh4yD8/ApeXOo6ygAV6SMaW1Hbd39vfMWrARSE9ELFv4URc0/ZQ/J/AHcKFU/ruEFnF63EqX+SYqN49KqzACvpKM1Qii6tGnHN2b2DEVqFflu1jfMe+5Db/juFJz+dxZ1vTGPUw+/zw9INwQ6tyvid8ykY3rMdfzvH2sHZZhx7oc1QhNhs/OfWC0sKW8XJ6dGmCc2S47z+ZUqKjaRPx8AULwv/SCJyknTuh1gzuhW9w3GDexcUzKny66rQU1EJ71ut1K1Hiv4bAlG3oGL+aT0aeTEq8QsIG249h2F1HY17CRU3wb93zeEX4n2kw42KOA8VOgD/6kEc1vkSPsFI+spqQHYcXfAj3hMbE/Jn+HGtumnM6d14866xZZboxkWFc9Pofrx73yU1plvq/NXbefDd70nNKDv0nZ6TzxMTZ/LDkrqRjLRrkkRspPfltqap6duxBUop7hozmP/dczFDurelQVwUzZLjuHJ4L7594jr6V2M/k7pOKcWDlw2z9r/0cMyDlw2TfiJBJg3NTpJ5sCeQ7+UIA8JHYcS/FpDra+22Gpq5doERbRVgGvFejteVHrK3WsxfWLSq5fhaERuEdEclfg240KnDwEyr4LjjGVadRvLPFcZjHhlr7ZLrjYrDaLTM76+jrsrOK8DhdBMfHVGj/qCapubCJz7igJdiwfjoCGa+cDMhtpPfgyjY3v1hMe/9uKTCr9VmWCNVnzx0ZbXHJWDB2h28+M1c9h/NKnmsYXw0D1xyBiN6n/gmnMIzaWhWTawcztdyVhNMb4nKyVHKBmFDrA+/jq9sErINnfu5NfWii3uS2LBGgEwIHWTtqKsMIBQSPkCnXQ86C++1/6bVrt61tsLVMtjbgWsTnkdYDLBXXzvumiwmMpyYYAdRgXW7Dpb5w1+RjJx8lm5MYXA1tlYPlJtG92fr/iP8tnJbScfU4j1umibF8dItFwQ7xHpraPe2DO7ahtU79nM4I4fk2ChObd/U6zJ4UX0kETkJSim0rW3R/i+ebro2CGlfnWFVGZ3/PTrzQaxBzeKEwLD+P+wcVPTdqJCyrcRVSBdoMBOd9XTR7rg+uPdVnIhEXAYF33l5oYmKlHeXNdnRLD9XLPh5XE1ntxm8OP58Fq3fyZSFa0k5nE58dATn9j+F8/qdQkRYzZguq68MQ9GrfbNghyEqIInISVKRV6Ozn/VyhImKuLza4qkq2rWzKAk5foql6HPHLIi+rcLXKiMRIsai/UlEVEL5a+tCyPvU++tCh0C4vMOsyfxdidCwDq1YMAzFkO5tGdK9bbBDEaLWkHGpkxV5OYSeTvlSKOtbq2L+ibK3rPawTpa1IsXbNI5hTdl4EjrId78TI9naO+f4a2f9Hzi8FPiG9EYlvINSkkfXZF1bNaJlw3ivK0qSYiPp16n2/fsQQlQdSUROklIhqIT/oaIfAKPUzrEh3VHxb6OibgxecCej8A+8r4Bxl9319zhKhaJivPcxUTEPlksmtJkG+ZPwWuzq2gBa2pfXdEopHrp8OArlMRmRFQtCCPkLUAWUCkVF34JqsADVYDGq4XKMpMmo8LODHdpJ8Keo1fsxKvIKVMwTpfqQFP26qWhU7LOoiLHlX+T4g5I+LJ7o/JPo4Cqq06AurfnvHWNonhxf5vHGCTG8dPP5jOwtO58KUd/J2HYVUsoAW1Kww6gaoYPAtR3PoyI2CDu9zCPaTLd6pugsqw172HBU1DVWa3XHb9YeOUZDCD/Ly263fjZ/K2oSp7UbdA6oCJSSluY10eldWzP96RtYu/MghzOySYqNomfbphhG5VZwCSHqJklERIVU5FXovM+8HGGiIq8BrDbwOuc1yP0AazTDAEyrRiTuOVT4SIjw3sG1hN333jeg0EYzdPZL1o7AOguwocNGoKJvtVbuiBpFKUWPtk2AJj6PFULULzI1Iyqk7K1Rca9g9Qwp3WzKBhio2Aklm87pnFch938cm1Ipqu/QmeiMu9CO3/2/bkhnsHfD86+mDUIGQOY9kPthURIC4AbHbPTRS9GOxX5fTwghRHBJIiI8UhHnopJ/gshrwNYObG0h8nJU0veoyHEAaPdRyP3Iwxms3io6+1W/rqfdhzEzHixqZFZRsaoNjAZgJIF7TwXHuAE3OvN+tHb6dU0hhBDBJVMzwitlb4OKfazc49q5CZ0/GRyL8V5cqsG1Du1K8bqMWbuPoI9eBuYhKq5LCYeoayH8cjh6rodjwOpke9SqSQk/x0tcQgghagJJRESlaK3ROS8V1YPY8G+TO9CuHd4TkZw3vSQhAC5U1HhwH0D7LGi1g2sLIImIEELUdDI1Iyon/6uiJAT8TUIAKFzh8SmtHZA/1cf53JD/HagIPy5mArKNuhBC1AaSiAi/Watj3j3BFx/1/Jx5FN+bB9rQ7t1gaw22lnjvYWJC+FmVDlEIIUT1C2gi8vzzzzNo0CAiIyOJj48P5KVEdXDvBPPACbzQAOVlPxEVje8GahpULEopVPSdeN5k0ICwESi77PUhhBC1QUATkcLCQi699FJuu63izdFELXPCK1FMVPi5Hp9VRqzVQK3MMuHjuVHh51nHR4xBRRfvCmxQZolx6CBU3IsnGKcQQojqFtBi1aeffhqAiRMnBvIyorrYW1k1Gjq/Ei8yIHQAhPT0epSKvhudtgQruTh+tMOAsJGokA6ljr/ZapKWPxXtSgEj1kp2Qk5FedtlTQghRI1So1bNOBwOHA5HyedZWVlejhbVTakIdMTlkPcpXjelQxV9mBA2FBX3Kkopqx27YwG6cD5oFyqkO4SfjzKiUKG9IP5tdOY/QWdg/WqagLaOiXu+/FVsTSD6Dr92xRFCCFEz1ahEZMKECSWjKKJmUtH3oAtXgGtd0SOeajVMiLjM2txOKbRrLzr9JqvOpOjXTudPguwXIP51VNgQVPhwCPvd2q/Gvd3aLC9shNdlv0IIIWq3SteIPPXUU1bBoJeP5ctPbGfURx55hMzMzJKPPXv2nNB5ROAoIwqV9AUq5p9ga+HhqKLkJH8S5E9Cawc6/TpwpxQ976KkCZrOQ6ffinZutc6vQq2OrtF3oaJukiRECCHquEqPiNx5551cccUVXo9p3br1CQUTFhZGWFjYCb1WVB+lwiHqRggdgD46xtuR6Nx3gTBw7/VwjAZMdO7HqPj/q/JYhRBC1GyVTkSSk5NJTk4ORCyitnHMx3t3VW0lIAUzKNmRt0JucPwM+E5EcpwFbM0+gKEMOsU2IdwWeiKRCyGEqCECWiOSkpJCWloaKSkpuN1uVq1aBUD79u2JjvbSV0LUCloX4rv/B6Dz8F7cCmiH16fzXA7e3PIL3+1dQaFpTetE2kK5rNVAbml/NnbD29JfIYQQNVVAE5EnnniCTz75pOTzXr16ATB37lyGDRsWyEuLaqBCuqK9bniHtdw3pAc41+B55ESBvb3HUxSaLu5a/jHrM/ZgliqOzXMX8smOBezKSeWFXldhKGkULIQQtU1A/3JPnDjR2iTtuA9JQuqIsGFgNMTzr5FhrZyJvArvIyIaFXmNx2d/2reStRkpZZKQY6/UzDu8gSVHtlYicCGEEDWFvIUUJ0wpOyr+TVBhlO+KqsB+Cir6HpS9DSrmwaLHj/+VUxA6DCLGebzO1D1/orxMAdlQzNh7Yiu1hBBCBJckIuKkqNBTUUkzIOJSq+8HgK0ZKuZBVNKXKMOqBVJR41Hxb0FI92MvNpqgYh5CJbyFUp5nCQ/kp6M99isBN5q9eV421RNCCFFj1aiGZqJ2UvbWqLhnIO4ZtNYeW6yr8BGo8BFoMwcoBJXgVzv2uJBIMp15Hp83UCSGSvGzEELURpKIiCrlT2JRPErizeLULXyz+w/WZe4pWSXjiYnm3Ga9/I5RCCFEzSGJiKhx3tz8C5/uXIANhdvLlAyATRm0iWrIWY27ez1OCCFEzSQ1IqJGWXh4I5/uXADgMQkp3lIPoE9iW97udxOhhuTUQghRG8lfb1GjfLVrEQaqwqW6YCUgA5I7MLhBZ/oktaNNdMPqDVAIIUSVkkRE1Chrj2tadjwNuLXJpa0GlnvO1CbLjm7nl/2ryXTm0SQingub96FTbNMARiyEEOJkSCIiahTDj2JXmyrfzj3P5eAff33G8rQd2JSBW5vYlMHklCVc3KIfD3a5UDqvCiFEDSR/mUWl5bgK+G7vcj7Y9itTUpaSUeh5aW1lDUjugM1LwqCA/snl28E/t24qf6XtBKwRk9L/nbLnTz7bubDKYhRCCFF1ZEREVMqk3Yt5Y/PPOEwXNmVgapNXNn7Pje2Gc1O7M/1avuvNVa0HM/fQ+gqfM1BE2sM4v1nvMo/vy0tjzsG1Xs/7+c6FXNX6dEKkqFUIIWoUGRERfvt+7wpe3vg9jqK+Hm5tVXO4tMl7234tWe1yMnomtOLRrmNRqDIjIwpFhD2U13rfQGxIRJnXLD6yxecewJnOPDZl7T/p+IQQQlQteXso/OLWJu9sneX1mI+2z+WylgOJsIee1LXGtOhLr8Q2TE1ZytqMFEIMG6c36MyFzXsTHxpV7niX6caatPHec8TpozGaEEKI6ieJiPDL2owUjjiyvR6T7y5k8ZEtnNm420lfr1VUMvedcp5fx3aKbep1LxqwGp+1jW500nEJIYSoWpKICA4VZDIlZQmzDqwh31VI25hGXNKyP8MbdS1ZaZLtzPfrXFmljstxFjDv0HqOFubQMDyWMxp2IdIeVuXxn5rQmtZRDUjJPVLh0l+bMjinSY8KR1OEEEIElyQi9dyGzL3c8eeH5LsLS27iK9N2siJtByMad+eZnpdjUwbNIhP9Ol/zyES01nyx63fe3Tq7pKjVrU0ibKHc1WkUl7QcUKVfg1KK/zv1Sv6+9D3y3IUlq2XAKnBtEZnEvZ3Pr9JrCiGEqBpSrFqPOU0X96/4tEwSApT8/+yDa5m0ezEAbaMb0SWuOYaHslCFoklEPKcltmHS7sW8XrSyBo4to813F/Lihu/4fu+KKv9a2sc05ovT7+bSlgOIsYejgIZhsdzc/iw+GnAb8aGRVX5NIYQQJ09prb1PrgdRVlYWcXFxZGZmEhsbG+xw6pxZB1bzr9XfeD2mcXg808/4B4Yy2Ji5j1uWvodTuzBL/doYKJRS/LfPDfSIb8Xouf9Hrsvh8ZxJodF8P+yf2I3yjcmqitb6pJcSCyGEODGVuX/LiEg9tiY9BbuPbqMHCzJIK8wF4JS4Znw44Fb6JbYvMy7SPb4l/+t3M/2S2vPn0W1ekxCAo4U5rE7ffbLheyVJiBBC1A5SI1KP+dNOHSgzHdMxtgmv9/0bqQVZpDqySAyNpnFEfMnzWX52Wc1yVl03ViGEELWXjIjUY32S2uEqVdh5PAW0jmpAQgWrTRqEx9IlrnmZJASgqZ9Frf4eJ4QQom6TRKQeO71BJ5pFJHrc20UD17QZUqlpjlMTWtEsIgHloajVQNE+pjEdY5qcSMhCCCHqGElE6jGbMnit9/UkhEaVSRuKE5OrWg/mguP2dfHFUAaPdB2LoVS5FTZGUdv2h7uMkRoOIYQQgKyaEVi76f60byVzDq4l11VAh5jGjGvRnx4JrU74nH+l7eTNzb+wLnNPyWOnJrTmnk6j6RrfoirCFkIIUUNV5v4tiYgIqL15RznqyKFBWCxNIxOCHY4QQohqUJn7t6yaEQHVPDKJ5pFJwQ5DCCFEDSU1IkIIIYQIGhkREaJIjquAn/etZFnadtymSY+EVlzYvDcJodHBDk0IIeosSUSEANZl7OGe5R+T4yoArKXLv6du5v1tv/LCqVcxuGHn4AYohBB1lEzNiHovvTCHu5d/RK7LgYaS7f80Gqfp4qGVX7Ar53AwQxRCiDpLEhFR7323dwW5rrI7EBezEhPNpJTF1R+YEELUA5KIiHpvweGN6AqSkGJubTL/0IZqjEgIIeoPSUREvec0XX4c466GSIQQov6RRETUe93iW3jcbweslvdd4ppXY0RCCFF/SCIi6r2LW/TH7WUXYrc2ubzVoGqMSAgh6g9JRES91y6mMfd3Pg8AW6mN+oo37bu69WAGJHcISmxCCFHXSR8RIYArWp9O25hGfLnzd/48uh2NpltcC65qfTrDGnWV3YKFECJAJBERoki/pPb0S2oPgNZakg8hhKgGMjUjRAUkCRFCiOohiYgQQgghgkYSESGEEEIEjSQiQgghhAgaSUSEEEIIETSSiAghhBAiaAKWiOzatYubbrqJNm3aEBERQbt27XjyyScpLCwM1CVFFXKZbvblpXEwPwOtPW8IJ4QQQpyMgPUR2bRpE6Zp8u6779K+fXvWrVvHzTffTG5uLi+//HKgLitOkst089nOhXy9exHphbkANItI5Lq2QxnTvK8saxVCCFGllK7Gt7svvfQS77zzDjt27PDr+KysLOLi4sjMzCQ2NjbA0QmX6eahlV+wKHUzmmO/FgrQwBWtBnH/KecHLT4hhBC1Q2Xu39VaI5KZmUliYmJ1XlJUwqwDa/g9dVOZJAQo+ezr3X+wNiOl+gMTQghRZ1VbIrJ9+3beeOMNbr31Vo/HOBwOsrKyynyI6jMlZUnJRm8VsSmDqSl/VmNEQggh6rpKJyJPPfUUSimvH8uXLy/zmv379zNq1CguvfRSxo8f7/HcEyZMIC4uruSjRYsWlf+KxAnblZuKieeZOrc22ZlzuBojEkIIUddVukbkyJEjHDlyxOsxrVu3Jjw8HLCSkOHDh9O/f38mTpyIYXjOfRwOBw6Ho+TzrKwsWrRoITUi1eSCef/mUEGmx+cVij5JbXmr703VGJUQQojapjI1IpVeNZOcnExycrJfx+7bt4/hw4fTu3dvPv74Y69JCEBYWBhhYWGVDUlUkZFNevLFzoUeR0U0mhGNe1RzVEIIIeqygNWI7N+/n2HDhtGiRQtefvllUlNTOXjwIAcPHgzUJcVJuqzlACLsoRXWidiUQZOIBM5p0jMIkQkhhKirAtZHZNasWWzbto1t27bRvHnzMs9Jg6yaqVFEPO/0Hc8Df31KqiMbuzLQWLUhraMa8Grv64iwhwY7TCGEEHVItfYRqSzpIxIcLtPNwtRNrE1PwaYM+ia3o29iO2lmJoQQwi8BrRERdZ/dsDG8UVeGN+oa7FCEEELUcbLpnRBCCCGCRhIRIYQQQgSNJCJCCCGECBpJRIQQQggRNJKICCGEECJoJBERQgghRNBIIiKEEEKIoJFERAghhBBBI4mIEEIIIYJGEhEhhBBCBI0kIkIIIYQIGklEhBBCCBE0kogIIYQQImgkERFCCCFE0EgiIoQQQoigkURECCGEEEEjiYgQQgghgkYSESGEEEIEjSQiQgghhAgaSUSEEEIIETSSiAghhBAiaCQREUIIIUTQSCIihBBCiKCRREQIIYQQQSOJiBBCCCGCRhIRIYQQQgSNJCJCCCGECBpJRIQQQggRNJKICCGEECJo7MEOQIiqVOAuZOmRbWS7CmgRmUSP+JYopYIdlhBCCA8kERF1gtaaz3ct5KNtc8l1O0oebxmZzL+6j+PUhNbBC04IIYRHMjUj6oSPts/ljc2/lElCAPbmHeWOPz9kQ+beIEUmhBDCG0lERK2XUZjLB9t/q/A5E41bm7y1ZWY1RyWEEMIfkoiIWm/OwbWY2vT4vIlm2dHtpBZkVWNUQggh/CGJiKj1jjqysSnfv8rphTnVEI0QQojKkERE1HrJYbG4vYyIACggMSymegISQgjhN0lERK13duPu2JXN4/M2pRiY3JFkSUSEEKLGkURE1HpxoZHc2nFEhc8ZKOzKzu0dz6nmqIQQQvhD+oiIOuHaNkOJsIXy3tY5ZDjzSh7vENOER7uNpWNskyBGJ4QQwhNJRESdcUnLAYxp3pe/0neS6yygWWSSJCBCCFHDSSIi6hS7YaNfUvtghyGEEMJPUiMihBBCiKCRREQIIYQQQSOJiBBCCCGCJqCJyIUXXkjLli0JDw+nSZMmXHvttezfvz+QlxRCCCFELRLQRGT48OFMmjSJzZs3M2XKFLZv384ll1wSyEsKIYQQohZRWmtdXRf77rvvGDNmDA6Hg5CQEJ/HZ2VlERcXR2ZmJrGxsdUQoRBCCCFOVmXu39W2fDctLY0vvviCQYMGeUxCHA4HDoej5POsLNktVQghhKjLAl6s+s9//pOoqCiSkpJISUlhxowZHo+dMGECcXFxJR8tWrQIdHhCCCGECKJKJyJPPfUUSimvH8uXLy85/sEHH2TlypXMmjULm83Gddddh6fZoEceeYTMzMySjz179pz4VyaEEEKIGq/SNSJHjhzhyJEjXo9p3bo14eHh5R7fu3cvLVq04I8//mDgwIE+r5WZmUl8fDx79uyRGhEhhBCilsjKyqJFixZkZGQQFxfn9dhK14gkJyeTnJx8QoEV5zyl60C8yc7OBpApGiGEEKIWys7O9pmIBGzVzJ9//smff/7J4MGDSUhIYMeOHTzxxBMcOHCA9evXExYW5vMcpmmyf/9+YmJiUEpVSVzFWZqMsgSP/AyCS77/wSXf/+CTn0Hgaa3Jzs6madOmGIb3KpCArZqJiIhg6tSpPPnkk+Tm5tKkSRNGjRrF119/7VcSAmAYBs2bNw9IfLGxsfILGGTyMwgu+f4Hl3z/g09+BoHlaySkWMASke7du/Pbb78F6vRCCCGEqANkrxkhhBBCBE29S0TCwsJ48skn/Z4eElVPfgbBJd//4JLvf/DJz6BmqdYW70IIIYQQpdW7EREhhBBC1BySiAghhBAiaCQREUIIIUTQSCIihBBCiKCp94nI888/z6BBg4iMjCQ+Pj7Y4dR5b7/9Nm3atCE8PJzevXuzcOHCYIdUbyxYsIALLriApk2bopRi+vTpwQ6pXpkwYQJ9+/YlJiaGhg0bMmbMGDZv3hzssOqNd955hx49epQ0MRs4cCA///xzsMMSSCJCYWEhl156KbfddluwQ6nzvvnmG+69914ee+wxVq5cyZAhQxg9ejQpKSnBDq1eyM3NpWfPnrz55pvBDqVemj9/PnfccQdLlixh9uzZuFwuRo4cSW5ubrBDqxeaN2/OCy+8wPLly1m+fDlnnnkmF110EevXrw92aPWeLN8tMnHiRO69914yMjKCHUqd1b9/f0477TTeeeedksdOOeUUxowZw4QJE4IYWf2jlGLatGmMGTMm2KHUW6mpqTRs2JD58+czdOjQYIdTLyUmJvLSSy9x0003BTuUeq3ej4iI6lFYWMiKFSsYOXJkmcdHjhzJH3/8EaSohAiezMxMwLoZiurldrv5+uuvyc3NZeDAgcEOp94L2F4zQpR25MgR3G43jRo1KvN4o0aNOHjwYJCiEiI4tNbcf//9DB48mG7dugU7nHpj7dq1DBw4kIKCAqKjo5k2bRpdunQJdlj1Xp0cEXnqqadQSnn9WL58ebDDrJeUUmU+11qXe0yIuu7OO+9kzZo1fPXVV8EOpV7p1KkTq1atYsmSJdx2221cf/31bNiwIdhh1Xt1ckTkzjvv5IorrvB6TOvWrasnGAFAcnIyNput3OjH4cOHy42SCFGX3XXXXXz33XcsWLCA5s2bBzuceiU0NJT27dsD0KdPH5YtW8Z///tf3n333SBHVr/VyUQkOTmZ5OTkYIchSgkNDaV3797Mnj2bsWPHljw+e/ZsLrrooiBGJkT10Fpz1113MW3aNObNm0ebNm2CHVK9p7XG4XAEO4x6r04mIpWRkpJCWloaKSkpuN1uVq1aBUD79u2Jjo4ObnB1zP3338+1115Lnz59GDhwIO+99x4pKSnceuutwQ6tXsjJyWHbtm0ln+/cuZNVq1aRmJhIy5YtgxhZ/XDHHXfw5ZdfMmPGDGJiYkpGB+Pi4oiIiAhydHXfo48+yujRo2nRogXZ2dl8/fXXzJs3j19++SXYoQldz11//fUaKPcxd+7cYIdWJ7311lu6VatWOjQ0VJ922ml6/vz5wQ6p3pg7d26Fv+vXX399sEOrFyr63gP6448/DnZo9cKNN95Y8renQYMG+qyzztKzZs0KdlhCay19RIQQQggRNHVy1YwQQgghagdJRIQQQggRNJKICCGEECJoJBERQgghRNBIIiKEEEKIoJFERAghhBBBI4mIEEIIIYJGEhEhhBBCBI0kIkIIIYQIGklEhBBCCBE0kogIIYQQImgkERFCCCFE0Pw/KJpSwkiaIecAAAAASUVORK5CYII=",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"clusters = hclust.fclusterdata(randpts,4,'maxclust',method='complete')\n",
"plt.scatter(randpts[:,0],randpts[:,1],c=clusters);"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"clusters = hclust.fclusterdata(randpts,4,'maxclust',method='average')\n",
"plt.scatter(randpts[:,0],randpts[:,1],c=clusters);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"You can even use a non-Euclidean metric."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"image/png": 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mkZmaHPRxWWkunr7qBK56/D2Wry9scp/NMDh6zGCSA9Mts75bGnYnXZuheP/7pQwb2LtFsQshhGh/JBHpArLSXFxx3H5cMnEsFTVuXI4kkh0t/9G///1SHp01D6A+UfAHpkiWrt7MtU++z2OXH9/scaap+WbJKt6d9xvrC8ua3e/1+Xlo5jcUl1dz2bH7UlnjDhuHaWqKy6tbHL8QQoj2RxKRLsRuM5oUhLaE1pqnP/ohZE8Sv6n5Ydla/li7hZ3zu9ffXuvxcdXjs/hu6WqUsmpBmp078N+XPvuZQ0fsRGZqMmVVtSFjMQyD7tnprXoeQggh2hepERFR2VBUzurNJUGTkDo2QzH317+a3Hb/W1/x/e9rgOBJyNaPf/ubxRw7dmjYzfz8psnRo3eNNvQ2s6W0kuXrC8MmSUIIIVpGRkREVNxeX8RjlFJ4Gi0VLquq5Z1vfwvbP6Qxv6lZubGI+6cezYc//EFhWWWzWhEFHLn3ruzSt2W1Ldvix2VreWTWt/UFsoahOHD3QVw6cWx923khhBCtIyMiIiq9czMjbo7n85vs1Kdb/deLVq7H6zejvoahFOkuJ9npKTx39YmM3rU/jcdFXM4kzpkwipunHNLS8Fvtq19WcsGDb/HbqoZ+Jqap+WLRCqb851XWFpTGLRYhhOiMZERERCXZYWfSPkOY8eWioEt1DaXITE1m3LCGniSRlvRuzdSaQ0dYS3h7ZKfz0EUT2VBUzvJ1BSQl2dhj4HZx2ykYrCLaW1+cjda62ZSU39RU1rj57xtf8cCFx8QtJiGE6GwkERFRm3rkaH76cx3L1xc2mW6p60nyn/OOaNKTZHD/nmH3l2nMZih65WZwyPAdm9zeOzeD3rkZbfckWmDur3+FrQfxm5qvf/uLwrIq8jJT4xiZEEJ0HjI1I6KW5nLy9FUnMPWo0XTLSgMgyWYwYeTOvHTdKYzYMb/J8d2z0jhg94EhO7s2Nqh3Hk9cPhlnO2rdvqagJGLsWsP6ouZLkoUQQkSn/bzqiw4hJdnBuRNGce6EUXh9fuw2AxWmdfsNpxzMXxuL+XtzMeiGpboKSHU5OHLUrhy0xw7sucN2Yc+zrVasL2T+76vxmSZD+vdk+A59Il4vLdkZ1fRSusvZVmEKIUSXI4mIaLVoWsNnp7l48dqTmfntb7z9zWIKyirJzUhl4pghHLvv0Ji/iZdW1nDD0x8y/4811l43yqpd6d8jm3v/cRTb9wq94d8Buw/i7te/CNnlVQH9emQzoGdOjKIXQojOT+l2vHtYeXk5mZmZlJWVkZGRmDoB0XF5/X7O+M8Mlq8vaJZM2AxFmsvJazdNoXtgmimYe17/ghlfLArZP+We84/koD12aMOohRCi42vJ+7fUiIhO68tFK/lj7ZagIxp+U1NR42bGFwvDnuOK4/bnhP2HoZS1MshuM1BAcpKdm087RJIQIYTYRjI1Izqtjxf8EXbVjmlq3v/+dy6dtG/Ic9htBteedCBnjB/Jpz//SWlVLX3yMjlk+I6kJjtiFboQQnQZkoiITqu0sjbi0uGK6vAb7NXpmZPOaQcPb4uwhBBCNCJTM6LTyu+WGXb5rYKE9SgRQghhkUREdFqT9hkacsVLneP33S1O0QghhAhGEhHRae22fS8mjhkS9D7DUOzavweTxg6Nc1RCCCEakxoR0Wkppbjp1IPp2yOLF+f8REllDQDOJDsT9xnCJcfsQ7JD/gSEECKRpI+I6BK8fj8r1hfiNzUDeubIihchhIgh6SMixFaSbDb6dc8mxZkU9UoZIYQQsSfj0qLTKy6v5uFZ3/LB97/j9fkB2CW/O/84ajT7Dd0+wdEJIUTXJlMzndC6glI+W7iCareHvt2zOWiPHbpsLURxeTWn3/0qm0sqmqygqWt0dsuU8RwzZnACIxRCiM6n3UzNzJ07l6OOOorevXujlOKdd96J5eW6PI/Xx83PfcwxNz/L/975hmc/+ZH/e+5jxl/3OJ8tXJ7o8BLi8Q++a5aEAPWNzqa9+hkV1bWJCE0IIQQxTkSqqqoYNmwYDz/8cCwvIwJue2kOH/zwBxrrjdbnNwGoqvFw7ZMfsODPtYkNMM5qPT5mfbc0bC8Rr8/Phz/8EceohBBCNBbT8foJEyYwYcKEWF5CBKzeXMJHId5QNVYX0cfe+46nrsqPa1yJVFRehdvrC3uMzWawZktpfAISQgjRTLsqHHC73bjdDSsaysvLExhNxzLnpz8xDIUZ4tO/qTU/r1hPUXkVuRmpcY4uMaJZoqu1Js0lS3mFECJR2lUiMm3aNG677bZEh9EhVdS4rQJMwtceV9V6Yp6IFJVX8c63S/hpuTUVNGLHfCaOGUJORkpMr7u1rDQXI3bsw8/L14fc/M5vag7Zc8e4xiWEEKJBu+ojcv3111NWVlb/b+3arlXTsC3yu2fhD9SEhJJkt5EX4yRk3tK/OfKmZ3j0vXnM/30N839fwyOz5nHETU8zb+nfMb12MP84YjRgTU1tzVCKA3cfxKDt8uIblBBCiHrtKhFxOp1kZGQ0+Seic+iInXAkhR7gshmKw/famZQYdhRdX1jGFdNn4fH5aLwqXGuNx+fjyumz2FBUFrPrBzN8xz7857wj6p+33WZgBHbkPXCPQfzrrMPiGo8QQoim2tXUjGi9dJeT608+kFtfmI1S0HgmwmYocjNSufDoMTG5tt80WbOllOdn/4jf7yfYLIjW4DNN3pz7K5dO2jcmcYRy0B47MGZwfz796U/+3lyCy5nEwXvsQP+eOXGNQwghRHMxTUQqKytZsWJF/derVq1i0aJF5OTk0Ldv31heuks6evRgMlOTmf7ed/y5rgCwRgDGD9+JyyaNpVtmWptezzQ1r36xkBfmLKCgrCqq47/69a+4JyIALkcSR42WxmVCCNHexDQRWbBgAQcccED911deeSUAZ5xxBs8991wsL91l7b/bQPbfbSAbisqorPHQKyed9JTkNr+O1po7X/2Mt79Z3KLH+fz+No9FCCFExxXTRGTcuHG04w7ynVrv3MyYnn/Ryg0tTkJshmLYwN4xikgIkWhej5dv3v6BxXOXAjB0v10Ze+xeJDmSEhyZaM+kRkS0ytvfLMZmqLBdS7fmNzUn7r977IISQiTMikWruPGIaRRvLMFmtwHw3mOzyemZxR0f3MCgPQYkOELRXrWrVTOi4/h7U3HUSYgtsErlskn7Mrh/z1iGJYRIgJItZVxz0G2UbrFWxfl9fvyBna5LC8q55uDbKNkS3xVzouOQEZFOrKCskvlLV+P1m+yc351d+/Vos3NnpCbX72AbjsuZxIgd+3DqgXuy185SoCxEZ/Thk59SWVaNDvLhxPSbVJZV8+GTn3LqjcclIDrR3kki0gnVenzcNeMz3p//e5NEYef87tx59oQ2WbZ62Iid+G7p6pD3G0px5qEjufiYfbb5WkKI9u2rN+YFTULqaFPz1WvzJBERQcnUTCejteb8+99g1ndLm41WLF9fwNn3vsbmkoptvs74ETvRr3t2/bRLYzZDkZ7i5MT9h7XonFr70GalFDgL0cHUVNRGPqYq8jGia5JEpJOZ9urn/Pb3pqD3+U1NRY2blz77eZuv40yy88QVx7NLX2u6x2YobIb169QzJ4MnrphMt6zo+pZo7++YJZejNw9Fb9kTvWVvzIoH0GblNscphIi9gbv3x2YP/XZisxsMHNY/fgGJDkWmZjqRX//ayJtf/xr2GL+pmTVvCVcdv/82X69bVhrPX3MSi1dt4vs/VuPzmwwb2Ju9d+5X30Y9Eu2ehy45H/AH/gG6BKoeQ7tnQ86rKCO2S5GFENvm6AsO5duZP4S83+8zOeqC8XGMSHQkMiLSibz25aKojquocWO2YNltWP6/Gdr7d849CKYeOYIxu/aPPgnRHnTpFYCP+iSkngm+VejK+9smTiFEzOxx0ND6REM1+vOv+/8jp45nz4N3S0BkoiOQEZFOZOGK9VEdl5OeEnWyEIr2rUCX3QLeHxtuVNmQdhGkTEGpKM7v/swa/QjJD9Vvo9OuQRkp2xSvECJ2lFJc8vC57LDn9rzx3/dY+4f1WtRnp94cf+XRTDjnwOheE0SXJIlIJxJtcnHcvkO36TratxpddCLo6q3uKEFX/BulyyHt4sjn8f6B9SvoC3NULfjXgbFj6PNoD/hWAhrsg1AqdjsMCyGCU0ox4ZyDOOzsA6koseq70rPTJAEREcnUTCcydsiAoKtYGktLdnDKgXtu03V05YOBJCT4vjG68mG0vyDieZRKBqKYIlLB98rR2ouu/B96yz7oomPQRRPRW8ZgVjxoJSciLrRZhfatk+JiAVgJSUZOOhk56ZKEiKhIItKJnDRudxSK4H/6Gpuhef6CX0ivnYJZeh3a0/LVM9qshNqPCJWE1KudFflkzoMinEeBbXuw5TePQ5vo0ivQlQ+DbtSxUZdD1aPokovRWjbYiyXtW4NZ+k/0lpHowgPRW0ZgllwYGOkSQojoSCLSifTvmcNd5x2BzWZgNPokohQ47Cb3n/wB/TLmgvcXqH0XXXwSZtnNaG1GfxGzmIhJCAbaH3wJcWMqaUdw7E/oX0ONSrsw+Kcq9xfgnk3wERUNni8D94tY0L6V6KJjofYDGqbWTHB/gS6ajPb8ksjwhBAdiNSIdDIH7j6IWbefzVtf/8qC5etQwF795jNp97l0S2/cyCyQTNTMQOtqsPcHlQnJh6Fs3UKeX6toeoOYKCO67q0q6z50yYXg/R7r11HX/1Np/0S5jg4eR/VrgI3QSZGBrp6BSp4QVRwdgdY+8PxgJYO2XpC05zYPfWvtAfe3YG4Bozs494mqxkaX3QK6iubffz+g0WVXQ94nMjTfhZUWlPHpi3PZsHIz6dmpjDtpHwYMkW0eRHNKt+M2luXl5WRmZlJWVkZGRkaiw+mQtOcXdPHkKI60Y72JKEg5E5V+NUrZmp+v9lN06YURz6a6fYGybRddjFqDdwG69gMwK63VN0kDUEYeOEYETWrMgkPAH7rFPABGL4zuX0UVQ3una95BV9wNZmHDjba+qIxbUc6xrTznu+jyO5uuXFJZqIzrUa5JoR/n+xtdGLknhMp5BeUY0arYRMf2zsMf8dhVz2P6TWw2A601fp/JfpNHc+3zF+NIloLyzq4l798yItLZeb4h/MhBnbrhdQ3Vz6CVgUq/ptlRuvZjrKmU8NM5WmWHqFVpTikFjpFgH4guuwlq34MaHZh0saNdk1AZN6GUq+FBRhb41xC22LWdNELTZhnUvImu+QB0Jdh3QKWcAo4xUY0Y6Oq30OXXN7/DvxZdci5kP41ytmxPH13zvjVq0eyOUnTZtYCBch0T/MG+VdFdxPcXSCLS5Xz52rc8cukz9V/7zIbXnq/fmk+S0851L1yaiNBEOyU1Ip2cVbDZiuHxqufQZnGQE1YTKQkBoOyqFl1Om5XoolOs2o8myYUPat5Cl5zfpPhUJR8V4Ywq9BtpHGnfSnThBGs0w/cb+P8G9+fokrPQ5TdFrM/R2o2umBbqXkCjK6a1aH8erf1hzhk4puI/1lRQMCrKni7RHic6Da01z9/6OqHya21qPnv5azau2hzfwES7JolIJ6ccuxO+T0cofqgNUuxpH4g1whKB+zO0d2n0l6t5E/yrCD5yY4Ln+0CSEuA6FozeIWKxWfUOruOjv34MaO232tebJTRNrurqc96A6lfCn8T9lbUSKPRVwPcn+JZFH5jnRzAjLK82C63veTCOPazps7Ac4Nwv+phEp7B++UbWLdtAuLxYKRW2HbzoeiQR6ewcY8HWh6iShyYMMJvv0qtcJxB5mgfAhq79MOqr6erXI8aja95qiMNIQ+W+BPad669X/xztg1A5Lyd+jxr3XPCvJfT3S6Grnw4/KmIWENWIlrkl+rga15mEPa4o6M1KOVBpF4V/bOpZKEPqurqamsrIO+wahhHVcaLrkBqRTk4pA7IeRRdPseoTokoisI6zN69wV/Z8dOqFUPVopCsHTWRCMgsI39zMBP/GplewbQe5b4N3UeDTu7ZqTZKGt4vVGtpTtxIo1IiUBv96K4mw9Qx+iNGNqJq+GaFXOjVj67Xtx6VMQekydOUjdQFgxWmC61RU2uXRxyM6jZ4DumNPsuHzhn6d8fv89N2lTxyjEu2dJCJdgEraGfLeQ1e/DDXvWgmJBghV76GspbzOA4KfL+1CdNXTgDvMVU1UkEQmJCMP/OWEftO1BX2ztgpd97D+tTua6BK/MImGc39QGWGmZxTYd2g0MhSFpD2sJnH+dSGurcDWG5KGhzyFUgrSLgHXCVAzC21uQhm5kHwUyt68AZ1oqry4gs9e+pq1yzaQkp7MvsePZqcRAxMd1jZLz05j/xPH8OWMb/H7mr+2KEORnp3GmGOkiFk0kESki1C2nqj0qyDdKiLVvr/QRZODtGq3PtmqzH+F7CehlAOdciJUv0y4Ph4kT4w+vpTJ6Ir/hDnCj3IdW7/UF9+fQDI490fZ8qK+TlypNCKOZqgMMHqEvls5If06dPkNwe4FFCr9+haNACllQMbN6JJ/BG5pHKN1HpV+s3VcpHPZekDaea0ph+6yPnnuCx684Al8Hj+G3QANr939LiMOHcb/vX4VKemuyCdpx877zxQWz/2dwvXFmP6GZMSwGSiluO7FS0hyJCUwQtHeSI1IF6Xs26Ny3wTnOJrUINgHo7KfQSUfGv7xaRcGhu63rj2peyO7DmXLjT4g1wlg6x/kfAAGJI1EGz2tFSjFp6LLb0eXX48u2Bez7P/a594yvpVRHGSP+IavUo5HZd4FxlbfT1sfVPaTLV66C6Cc+6Oynwx8zxufsx8q+wlUcvDRMLFtfvhoIfee/Shet8/qreH14/dZyfzPny7mjpPvT3CE2y63VzYPfz+NI847GKcr8GFGwfDxw7hv7u2MPKw9jl6KRJKGZgLtLwRzI6jMFk2naH8huuK/gX1lvNaNtoHWlI4yABvKMRoco6Lrl+EvQpffuNUSXhskHwOpZ0DxyaBraT6dZIDzUIzsB0OfW5tYU0nJMakfqdvwTRkNnWfNopPAG2k/H4XKehicB0eMS2tvkM6q2/ZZQmsdWFZcALZuYB/SLuprOqtLR9/AHz+uQJuhX3an/3w3g3YfEMeoYsfj9lJWUE5KhovUDFnO3ZW05P1bEhGxzbRZYTXX8m+CsttAb6ShXbsf7Duish+PvtOqb51VgKpskDQSZcvDLL0Oat8lbM1F+q2o5HHWmzWmVQuBHV31JNS8A9SCSgfXCajUc1s2YhMsTq2h5m101TPgX27daN8ZlXoOJB+NLr0U3J+Gj7mO6yRUxm2SBADavwV0BRg9miR2HV3J5lJO6HVe2GNsdoOTrpvEmbefFKeohIgN6awq4koZ6WidC8WnB/YfgSYrRXwrrVU7ue+jjMifipS9D9gbquq19lndViO9oVfcim62UMdG/WoOsN7gqp+z2snnvo4KFMBqXWslFdWvWStZjByU61hIOQllZDW7lNYaXX4r1LxKk6kt359Wx1Lv7yjXMWj3JxGfLwA1M6wVP65Ijdo6L+3+Fl35oJWEAuBAJx+FSr8CZeueyNDaRG1VuOJui1KKWlnaKroYqRERbUJXvxJmebAf/OuskYlWnbyG+qmfFvPTfCrHD2aBlUjQ0NVVl98Gvj+sFSr+v9GVD6ALj0H7NzQ/reebQBICTYs9A9eqfgat0gMrT6L5MzPQVc+24Hl1LrrmA3TJ2eD9tdGtHqh9B110vDVK0sHl9s4mOdUZ9hifz0/fXTvHqiO/z09laVV9DYxof1YsXMVXr8/jx08W4XG39jV228mIiGgbNbOI2Pq96hFMcwsq4/agG+qFpFJApTYabWkLfmvLev8mdOX/wPc7zVe4mGBuQZdegcp9rck9uvplwu/hY4OaGajsJ63uqt4FEeIxwbcErc1trvvoaLRZjS6/KfBViKSx4j5U1l3xDq1NOZIdHHb2gcx69JMmq0nqKUhOcXLASWPiH1wb2rhqMzOmzeTTl+biqfXidDk45PT9Ofn6SXTv24J+NyJmli1Yyf3nP8bKRX/X35aencppN09m0qWHx32KuGu94onYiTZJqHkDXXYdOtCcTGs/Zu18zOo3Md1fB+0yqpQt0K69pd1hI9Foz8JA/UiohMIP3oVo7+9Nb/YuDfOYusf9HkigwrVob8ygVfsCtQHt+wuz/FbMLWMxN++FWXw2uvbzFu1h02rujwO/P6Gu5Yfa9+oLgjuy0289ge0G9cSwNX3prVvaevWzF+FK67jLd1f/vo4LR1zLJ899gafW+oTtrvHw0dOfceGIa1m3fGOEM4hY++vX1Vy1/82sWrymye0VJVVMv+I5Xp02M+4xSSIi2oZ9AFH/OtW+iy7YH7PgaPSWkVB6OpTfACXnoDcPxSy/p9lDVOr5VtOztmZuJKppH+/CrQKK4s1CJYN/daDnScSDwbF3QopVtXsuuvAoqH7N6vKqS8HzHbp0Krr8XzFPRrRvFZEHZ71gbop8Lu8ydPXL6OpX0L4VbRJfW0rPTuPBeXcw6dLDSclo+B0aNm4w93x2C/sdPzqB0W27e858hOrymmbNzPw+k4qSKu47b3qCIhN1nr7xFbweX/BROeCFW1+nrDDaD09tQ6ZmOhBtVkPtLHTNu9ZGavZ+KNeJ4ByX8OF8lXIKumxh5AMb8/8R5EYvVD+JaW7AyGroqaBs3SD3dXTxGdYOtm1BZYB9xygP3ur7mzwBqh4j9HSUgUo+rAWjIRqVem6Ux7YdbZaiSy7GKi4OtjHfS+DYE1xHxiwGpdLQ0ezorEKvoNH+TejSKwNTYIq656IdY1CZ/93mFVJtKT07jan/PYNzpp1CWUE5rrRkUjNTmxxTtLGED5/4lPkf/ITX7WXX0Ttx1AXjGTisf2KCjsLKX/5m2Y+hkz/Tb7J47u+sXbae/J2iW0En2lZpQRk/fPhz2D6LpmnyxYxvmXjxhLjFJSMiHYT2b0YXHYMuv9nqTeH/C9xzrU+tpRdbPSYSKfkIcIxru/PVfoDp+a3JTcrWC5XzAm01RaNSz0Ul7RHd6IZjq3l712QgieBTKTZrmXDKCYENB6MY5Uie2KrGZNus5m2s/iqhXpmaFtFqsxJd/RpmxX/RVU8HL+RtqeRDCV9fZIB9aP0Kp61psxJdfFqjUatGz8XzPbr4NGtVVDuT5Egib7vcZknIr3OXcuaOl/DSv97kzwUrWbV4DR8/8xlT97iatx/4IEHRRrb1UH8of/+2NsaRiFBKNpdFbPZssxkUbSiJT0ABkoh0ELr0ssDeINDwmxT41Or+DF0ZaRM60NqN1jUxiU8pOyr7EXCd0nYnrfxf8+vYekL61a04mRH4F0hiXKdB6vkoIxVSTiV0smAD54FNGr1p9/dQfAph38AzbkUZOSgjB4woNplL/2fUz6Qtac+iCEeY4PsNrf3o6jfQW8ZYyXDV0+iKe9AFB2CW3WotsW4lZe8PSXuGjUGlXxr67pq3wuxy7Af/Sqh5r9XxxVN5cQU3HTUNd40H02xIzuqmOqZf+RyLvvgt1MMTypkSfkVQHYcr+NYRIvayumdG/Fzk95vk9sqOT0ABkoh0ANq7JNChM1RxpIbqF9E6eJ8CXfsZZtFJ6M1D0ZuHYRYchq6eEX77+XDx+DdiVjyAWXQqZvFp6MrpaH8hSiVhZN4K9qG0ya9WiDl+lXIWGFF2nlTdIes5SDnF+uSdciYq70OMzIa9VFTa5eA8MPCAutGWQPz2nVGZDXvgaO9ia5mpuTnMRTWU32Tt52NWB3YWDhskqvbj6J5PW1PRFMgqdO1sq+sttVjJlw9rFENDzavo8mmtDkHXfh6+A63zQJRz/9CPr5lJ+I95KnBM/C3/+S+eufEVHrnsGd6b/glVZeGLumc/9yW1le6QnVcNu8Gb97XPpGrPg4bgSA6/h4wrLZnd9t81ThGJrWV3z2TEobs3K5ZuzDAM9j8xviu3ulyNiDYroOZdtG85qGRU8iHtZtv4kDzzsd4YwyQOuhx8yyBpt6Y3Vz6BrryXJomBf5X1qdazADLvblF9ia79BF16RSAWKx7tWQCV0yF7Osq5DyrrHnTRyYH6iG3oIWA0DFlr7YHaT9GeedZ1U46BygcinECBczhG8hhIDv2HpZQDsh4Bz7fo6tetkScjD+WaCMmHNNn8T1c8SPDeJI2ZoGvRlQ9bSU7EYlgb2r+uWTqgzWLwLAQ0JA2z6mTamHKMQdd+GDY2kkYGvtcNtRdbRQo1L6PT/tHixmNaa3TFvWHODbjnYvqLUf7V4PsLjBRw7NvQddUsinQVMAvRvnXo6ucadqC2bYdKORlSTkFFMz0X5fNZ/vNfrFq8hvcem82yH1Zgs1srYnw+P4/98wWufGIqB526b9DHL/x8cdjiYNNnsvCz9jkikpqZyqRLD+f1e94l6FNQcPyVR+FKTY57bKLBOXecwi9fLgGtMYMkvKfeeBzZ3TPjGlOXSkR0zYfosmsBD3WffHX1s1bTqexHUUZ8h6Pa3FZ//dr7ZyAJgaZvnIHjamdZ+8K4joju9L4VgSTED82aeLnRJVOh2xyUfXvIewdd9RRUv4H1KboVXMc1XLf4nMAKl7oRCz/Wr2+4KQGNcobfvK+OUgY490U5g79BgFXYiedrIk6y1sVX+xE6LZopFz/4lqF9a1D2voG+Gv+G2ndoeH4GOvkIVMYtKKMNtztIPhIq/mutlAn6vPzgOhzKb45wIg3uOYFprhbwrwR/pNUtPig8Eq0LG93mRKeei0q7xKrDMQsIVziMkY0uOjrQHC+QHPvXoCvuhpr3IefFbW4nv/S7Zdz/j8eb1UA0XkHiqfFw1+kPkdU9g+GHDGt2jmgWKOmofv9ip6qsit+/X4HpN9lxxPZkdWt40zrr3ydTVljOx898gS2wszDK+h4c+Y/xnHbz8YkLXAAwaI8B3Pv5rdx37nRWL11Xf3tKhotTbzyOyf88Ou4xdZlERHt+RJddUfcVTd7AvIusN9GcGVFsPGaC53sIjKjgHBf79tNJw4nYLEylQlLTFSC6Zgbhm24Z6OqXUdEmItUvYX3vQnwqxouunoFKv9wqLM34P3T6TeiyG6D2raiuUU+lo1zHBgoRT7dWCcFWzyXcaIsNbH0h+eCWXTccs5zokpA6fpRSaMeYwKhWqJ+hBs98dOEh6OTjrU/9voVbHW9C7YfWktTc11CqjT5Vur8E7aX587JGKFT6tWDfKYpnbUBr+nyYpdEdp7ce9XBD1SNoXYVKOQFd9lO4i4B/TdMkpOHE4PsdXflfVMYtUYe9tWULVvLPA2/F5408Amgog5f+9WbQRGTIPjuz4JNFoadmbAZD9tm51XFuC0+thyevfYkPnvwUb6BHiM1u48BTxnLRg2eRmpmKzW7jqqcu5LgrjmLOC19RvKmEvN45HHLGOPruLCtl2otd996RJxffx7IfV7B++SZSMlzsefBQnK7o6nzaWtdJRCqnY01PhCho8y60lv45RoY+h+cXdNmVgcK4uqkSA+2ajMr4vyZD+G0qaRjYB1vtx4PGb0DKyc2Hl71LQhxfxwTf0ujjqP0s8vncX0L65Y1uKgB7Pth2aNgYLqIUVM4Maw+bqhcDQ++hkp+6AlQvDb/OPrAPRGU/1bY/EyOHyKMwjdmtHY3TLkUXf0/Y6Yf6Uao3wpzPb71pVj1ttTyv/dB6c7Vvj0o5DVzHolT0f9K69vNGyXmQeFyno1LPQfsLiDg1iB/s/aO+dr0oN0IM+X2rfh6d9wkkjQjUmQTZmdm+C/iWhDm3CdVvodP+aRUvt8KT17yI32eG3VW3/mqmyW/f/EFZYTmZeU1HtyaccyAv//tNvG5v0NER029y3OXRfXBoS6Zpcuux97Bg9i9NnqPf5+ezl7/mr19X88A3/yY5ULDaf3A+5951KmWF5SilyMhNj3vMIjylFDvvtQM777VDokPpGsWqWteA51vCv4na0bWhNyizpgemWBuiAQ0veCbUvB6Y8okNpRQq6yEwutG0sDDw43PsDSlT0FUvYJb/J/BGtQmiKUaNsmBV65oo5uKh8Zu0rnoaXbAfVD4E/lVE/euWeQcqyfrj0O7ZEQ42QaWgMu4A1wmQcioq+1lU7qyQyz1bSxlp1jLlqJYP2yB5AspIQTn2RGU/BiqrLaKwvp81r4MuAzzWtE75TeiSqVEv47ZqM+6u+yr4QTWvo81KqzbFeSChn7eynpvzgJY9Fawl2Tj2CXPuiGdA1X6MynkaUk4GGn+iS4aU0yPEXqfWWhIfwrrlG5n//k/88uUSvJ6m3+Mtawv55cslIRtEhVITZHO77B5Z3PzGVdjsNmtqI6Du/0+96ThGHTG8RddpC99/8DM/fhx8pMb0m/z1y9/Mef5LwPrdev/xOZy182VM7nEux3c/h7N3uYwPn/w0Pp16RYfTNUZEdA1RDanr6tB3VT6C9ak72IuNhtoP0N5/oJJiM2yq7PmQ9x7UvGmtADBLwdYXlXIS2rcBCg4KNIUyrP9W3ANGNFNGPrTWTaaktH891H5itd22DYDkQ6BmJpFHAgxwjLDOUfMOuuI/je6L9kXajnI2Kiw1q4n4s9Nl4NgLI2VylNdoPZV2Gdr9VaAQN9RzsoFyodIutvpX+NeDbSB0mwvVL0Hlf0I8LhpbLd1ufJvna6h6GtKmRj6Nb1nYN15LLbg/B9fRqPTr0J6fghQgB1YeZU5r9eiTyrgRXTQZdC3hPywEY6DNIgzlQmXcgk670mq/rxTYd0UZaVZCHNWUWvMVH2v+WM8DUx9n8dyGFv8ZeemcdtPxTLxkAkopSjaVtjBma/VITs+soPeNOmI4Ty6+j3cf/ph57y3A5/Gy6947MvGSwxk2bnCLr9UWPn7mcwybETbZ+uDJTzly6ngemPo4Hz75WZPPTOuXb+T+fzzO8oWruPSRc9v34gARd10jEVFZoLJBh2vS4kfZBwW9R2s31H5MpJoEXftezBIRAGVkQuo5qNRzGmKrngFV9zU6qtELRRQtscGHdn+NSt4PrT3oslug9m2sVxED8EF5BqhoqqhNlOtU69N2kB4gUTG6B96QApIGg+83IiUjuvplVMYNrbqkNisDm97VvXmlhDxW2fugs+6HkjBdUI0cyHoEXf1qYOQikOAafazi0JjR6KoX0PadUbit6akQv9MNNTfhGPXHKXtfyH0LXfnfpn8LSXug0i5HOUe1Pmxbf8j4N1Q9C75fIx7elNlk5EsZ6bB1LI59gQjJn9Ed7E2HqDes3MSlY26gpqLpyEV5YQWPXv4slSVVTLllMtk9WrbCwLAZHHb2gTiSQydufXbszUUPnc1FD53donPHyubVBWGTEK1hy5pCFsz+xUpCoMmfbN1AyPuPzWbfY0ex58G7NT+J6LK6xNSMUobVRyLs07WDa1Lwu3QVUX1Si+rFve1o7UNXPrjtJyr9B6b3d3TZ/wWSEI2V0ARGQHQ5mFF0QzS6WVMq/pWBOppWMDeji05A+60VEirlJKIazXJ/0eJLabMas/x29JbR6OJT0cWnoAvGYFbcYyVlZiW6egZm2Q2YZTeja+dYjbtq3o7wHAqg7Hqofr7pKJu5Dqofa3GcLXtShVB6Prr0EnTh4ZhFJwTfc8XWO4qTmU2OU/Y+GFn3o7rPR+W+j+o2FyP31VYnIVoHEqeC/aDsikASYlhdbB2HEPXLU/JRYe9WSTuCYyzhpmdU6nnNdoR+/pbXqK2sDfkG/NK/36RoYwnd+3Zj6H67hO3NUMewGfQe1DNuq0f8fj+rFq9m2YKVEXuYhJPTKxvDCD+Kkd0jk1mPftxkSmlrNrvBrOmhp8BF19QlEhGw2nlj34XmT9lq6KQy/hV6+a5KByL3GVBBXty19lkbcXmXtn1XU+/CKOs2IvFD0QlQG6kxVDiqYd+WMFNcUcViFlpLfwGVtGuUtRUtG9LX2oMuOQeqX8HqkFp3RzVUPYUuOhW9ZazVb6VmpjUlVnoRessBUPt+hOspKxlr1fdSYQ1UBnvRb8VwtncxuuhEtG910zPZ+wW6mYZ5CVBZEKSRmDIyUUk7bnMNjq68D13xbzAbL8sNrErzzCWq6bzUC6Nataay7gN7XSMtW9P/uqZYtSSN1FTW8NXr3zXbvK1J/Frz2UtzATjvP1OsHXTDvFm70pI55qLDePDbf5ORE9viTa017z02mynbX8T5w/7JxXtdx+Se53LfedMpL65o8fnGn75/0J4TdZShOOysA1n165qw3zO/z2TVr9G1ghddR9dJRIxUVM5LkHp+02mGpBGo7GdQKceGfqxKgpRjCV/wZjYZUdHarC/W1EVHoYsmoreMxiyf1nYJiW79J5zmgndljZ5GBfp+YOvLts36+a1CybrxXOfBhP9VtQWWOLdAzXvg/YmQNT++X4C6hMpPw+jQZiInGNEmIMGSYhtkPQTOw7a6X1mrp1rMD7oaXflws3tUxk1YdRFbx6GwkvNbY7YSTPtWQ9XjIe71E93vo9PqIxIFZWShcl9HZT1mbVjoGAspJ6Jy38HI/L9mNQulBeX4feGTW5vNoGCd9UFgl1E7cPecm9luh6bt/NOyUjn7jpN58a9HeLPgGS584KygScjm1QU8/s8XOKXvVI7NPZMr9vs/Pn/1myZt3lvi2Zte5aELn6RgbcMHFa/bxyfPfckV+/5fi0dHxh47ip1GDgo66mOzG/To143DzzsIV1rkZeWudGloJprqGjUiAcpIRaVfiU671JpGUcnWnHI0j029AF07JzACEeQFKvVCVGApotY68En69abH6GpruaH3F8h5Ydtf5G39t+3xbcaApKGQPB6wXvR18uFQ+wGt7qyqK60iY5WCSj0NXftmmIP91vLVlpy+Zgbhl9PGmrJ+fv41WEmOAscYVNplKMcwSD7YWvnk+dGKMWkPsG2HLjwqUGTaku+r3yqm1rc3WeKtkoZA7qvo8jsCSVmAbXtU+tWo5AODnKtt6Jq3iLwkOAL7wBYVPSplg+QDo3peGTlpKEOFXY5rmtrauyNg6L678MzSB/j9++VsXLmZtOxU9jhoKA5n+LbnS+f/yXXj/2XtLxOYBlr63Z/89s0ffDvze2549XJstuhXFa1dtp5XpwVvaW/6TdYt28hb93/A6beeEPU57Ul27vrkJv577qN8M/OHJn82Q/bdheteuITUzFT2P2EMa257PeToiTIU+0+Ob/vwjmDFwlV8MeNbKksq6TWwJ4ecvn/c93tJJKXjsJ7q0Ucf5Z577mHjxo0MHjyYBx54gH33Dd3Bsk55eTmZmZmUlZWRkdGG3SRbSfs3osv/Za0kqHsBNfJQqRday0YDL4ra8zO6+KSw51IZt1ntpbeRWXRqmH1otvGFPiqGtUw1419NOlNqfwG6cDLoTVvF0PgTVbjYklE9FtW3n9dVz6Mr7qBpgzbr/1X6tU0LeP1bAj1XksCxR9DmX+aW0W00rbUNHAegsv5rxWFkRNXZV/s3WA3e/Gto+PlGl1CpbnND72DrWw3+DWBkg32nmK5q0GYxuuCIIE3KWioDo+eCNokpmFsm3c38938KXaSp4MWVj9CzvzU1pLWmeFMpPo+P3N7Z2JMif87zuL2c0ncqFUUVQd+8lVL8497TOe6K6Iucn7z2Jd68772wxaXZPTJ5feNTUZ+zsU1/b6lfrrzrmJ3ot0uf+vtKNpdyzq6XU1Ve0+z6hs0gLSuVp5fe36Qba1fmrnEz7dSH+PadH7DZA92+TROU4rz/nMbxV4avf2rPWvL+HfMRkddee43LL7+cRx99lH322YfHH3+cCRMmsHTpUvr27Rv5BO2IsvVCZT+K9m8G3ypr+/ikwc2aSOnq1wnf0VShq19pk0REZd6GLjohSNdIA+vH69nma4S+eA4q753gb25mCSh/kD4lNkidClXhVtXYwDWpIQnRbqtWwehptaX3zAc0OEahUs6sL5bU/gJ0+e1Wq/G6JEelolPOQqVd1LQYUWUDMUpEbNtHsTTWCCQfadCC1uLK1hvyPoDaj62+N3UjR97FhE/sbKBCvxgoez+w94s6jtbS2ocuPgt0cRucbVtqkSI747YTWTD7F3weL6Z/qyRBwcSLJ9QnIV/M+JZX7nyrvr17Rm46R194KCdfPyns6phv3ppPWUF5yPu11rz94AdMuuxwDCO6mfSNqzZHbKxWsrkMr8dLkiP8aE0wPft3p+eZwetysntkcfdnt3DTkdMo2lBS/+bq9/nJ6ZnFHR/cIElII/ed9xjfzfoRYKupQM3j/3yBrO6ZHHzafokJLo5iPiIyatQo9txzT6ZPn15/2y677MLEiROZNi38jp3tbUQkWmbRSeF3EwVQKRg9FrXJ9bTvL3TFA+CeTf0nZMe+qPTLrb1h/KsjnKFZcET+hK0gaXeM3Neax2OWogsPA7OM5smYASrF6oQZtCDRZk3H5L5j7Q9S+RDUvNFQD2PfCZV2Mdj6WFNluhpl3xHt2AdKTrM+1QdLAJOPw8hq+H3TVU+hK+6J4nm2gmMCeD4J8tyaUtlPoJzjWnRqbZaA9xdrPaRjGMrIQfvWoAsPIfRzsZqrGVn3hbi/5bRZDjXvoH2/Aw7reTj3a7bypNnjaj9Bl0ZT11E3IhPqOSmw9cXoNif6oFvh9++Xc/eZD7Nu2Yb625KSkzj+iiM54/YTsdlsvHLn2zx706vNpnKUoRi67y5M+/imkNMz/7v4KT548lP8EVrDv7bhCXJ6RjdUf//5j/HJc1+ELRp1JCfxftXLMRv58nl9fDvzB375ailKwW77D2afiSOjGiXqKjb+tZnTd7g47K9474E9eW7ZQx2y70q7GRHxeDz89NNPXHfddU1uHz9+PPPmzWt2vNvtxu1uKFIrLw/9SaFdM7KIOC2i2q5qXtm3R2U/ZL05mAVgZKOMHOvOlFPRFdOI/IZrszYPS7saqh4C358Rjtfg+wOz/HZrVMLeaHSr5q3AUuZg1zStT/HepY3acjdulOYAI8eK2fsHmBto8n30LWv0RmZgNXDzYRVd+kI/z9q30N5TUElDra9dJ0DVS2BuJmiytC1TWp6PwHUS1MwIcYDNqqlxRJ6erKPNamtqqkljOTs6+RhUxo3gOg1qXgx+LeW0krc2omvnoEuvwiootVad6ZpXwTYIcp62uqWGemzNB0T3/XUQqWC1LUYUI9ll1A48s/QBlnz7B2v+2IArLZm9JuxOaqbVCn7dnxt49qZXAZqNQmhTs3ju73zw+BwmXXp40PNHs+S3JccBHHDyWD586rOQ99vsBgeesm9M39zsSXb2P2EM+58g9SChzHv3R2svqlBjARo2rNjEmj/WN5n+6oxiumqmsLAQv99Pjx49mtzeo0cPNm1q3mxr2rRpZGZm1v/Lz8+PZXgxo5KPJOIwueuYtr+ukYGyD2xIQsBqe520O+F/1MpKXrIfx3CNR+W+B+l3EazTZBO6BqpfRRcdjfYsari59iMid0PdAt4frDevtKsCUyUANdYIjvszq+9G2O9jo14nBNu4rTFboEDSoowMVO4rVkJg3UL9p3DHXmD0Dx9/WIY1VZLzaqPnVdcgDqsoNfvJiKMHdbT2WkuNa96iadLmg9qZ6JKzIf1qSL0I2Koexr4DKucVa0fkNqC9iwOJoBvr+91oRZF/Fbr4zPBt5nUp0SR5Kvdla1ltUAbYhwRauseeUoohY3fh8HMP4oCT9qlPQgA+fPLTiEnCu49+HPK+4YcMCzsaopSi3+D8ZnvShDNs3GCGjRscNC7DZpDkTOKEq+O/w6poqrbKHdV0W23Vtq5obP/isnx368x765bida6//nrKysrq/61d28qmWImWPD7QUyPYG40NVFqLV3m0llJOVM5zkHquNSXS9F6ro2TaFai8DxverDxfQ8UNRLcyww+61uqxUfcGZLZg7t6/HKqeCrQOb6ytp0z84N/Y5BZl642R+zoqdyYq/UZU+k2ovA8xcl4Ax+60/s/DBN8SVNJQVPd51hRM2qWotKtQue9j5DxtdcmNVu0nYZYam+BdhHJ/iJF+mXW9rIdRmXejct/GyJtl9WJpI7ryKUJP3fmtPYXcn4c+gW0AEfd9MfqjknZDZdyESr9pq60KXFZheM4LzTd5TIA1f6yP0HFUs2HFppCfekdO2J3eg3pihGgCprXmxKuPaeHqIMXt717L3kdaS9qVoeqbjOVtl8Pdn95C/k6yE26i9RvcJ+IScXuSjd4De4Q9pjOI6dRMXl4eNput2ejHli1bmo2SADidTpzOxGxD3JaUckD289YnR+8CGl54/WDrY71RtPGGbOHjcYF9IFpX03RYXFtTOZ7vINVqJa21D112vXVf1MmAaZ3H/TkkHwpJOwc2uYs2kSlr4TNqDRsYuUHvUUmDrVbyjRnptKqBWNOTWKMeznHWv1bSNa8TfjpDoSufQNd8ZE012XpZPV3suzQ9j3dJYJ+iAjC6o1yTWpSkaK3B/SmRtzqYg0o+tOEx3gXWTtAqCZz7Qc2rYR6vUKmnWv+nFKSeDimngm8l4AXbgLAt+OPNle6KuAeL0+UImUjYbDbu+OAGrj7wVgo3FFspnramT/w+kxOuPoaDp7S8WDEl3cVtM69h3Z8bmP/+T3hqvQzaYwDDx+/WoqXAInb2PnI42T0yKS0oD1pcbNgMxp20D+nZ0Rezd1QxTUQcDgfDhw9nzpw5TJrU0Oxrzpw5HHNM209NtCfKlovKfQXtXQrurwG/NUXiGB33wiPtL0KX3Rj4ausXTG2tQql+EVLPsWI1C1pxFTvauxiVfCgq5WR07fvbGHVb86OSo/+dU8mHoauD1VxEw7D2YFHR/XlZexl9gvb+DsqBch6I1h7rZ+JdEKi3CTedoa1VOv6/reN8y9HuL8AxCrKfQGsTSi4C7zyspDiwOWL18+jkSajMO6KM1Y81BRaOSd1eQdq7DF16WWAFkUF9Ymv0BTNEd02jO9o5rkkKqJQNknaMIr742/fYUXw549uQ99vsRsQ6iT479OKZ3x/g05e+Zu4b31FdUcP2u/XjyH8cwk4jQ+wVFKU+O/bm+Cujaecv4s2eZOe6ly7jxiPuxPSbTZJZw2bQLT+X8+8ONT3ZucS8hPnKK69kypQpjBgxgtGjR/PEE0+wZs0apk6NYofQTkAl7QptODTeKjVvEemNTFe9CClnb9WfoiU0dTUlyjESnXImVD/XimBjQUHSaKv2I1pJIwLFtAsJPQIQ6vtkWlsKREG7v0WXXh4YFbJj/SzqVpi19OdQd2wgXs+P6KJTrZ126xMIf9P/1r6DtuWh0q+OeHal7Ghbv8DvSJhSf/uOaN86dPEpjdr9N96McR3Y+oG/kqZLqJWVBBceik6/DpV6ZsSYEm3MMSPpt2sf1v25ofkqFQWG3cbkqyL3gnCluThq6niOmjo+RpGK9mjPg4by4Lf/5uV/v8V37y1AmxpXWjKHnX0gp9x4bJdZ6hzzROTEE0+kqKiI22+/nY0bNzJkyBA+/PBD+vWLfc+CRNG6BsxyMLJQKvFTTdr3R+SDzA2AOzAl0ZoVI36Us2EIWaVfj1aZ1gqchHUvraPBt8haCpx2cVRFokopyH7Mml7zfIc1kqAAH6g0SL0Mqh4O1LbUPb9Ag7W0y1HJB0WOyvsHuuR8GgpQfVsdsa3N6MzAzsVho4CqF9GpFzRpSBeKSpkSaCwX7pjJ6KonA0lIsCTOtAqS7YPB13i0p2E6UFfcCbaeqOTDIsaUSPYkO3fN/j8u3us6ijZstemltkZM+sZhxYO7xk1ZYQWpmSmkZrSfqSsR2Y7DB3LbzGuoqaqltrKW9Jy0LrfMOS7P9sILL+TCCy+Mx6USSvtWoCseBvcnWC/ADnTyUaj0i+vbvyeESiZyvUNgszXngVgjG5GG4BuzQdKQwOqcAM98qJpO/JMQBThpWNURoKuh6lG0fz069Tyr/bxZajUIc00MunGaMjJQOc+jvb+haz+1epmYVeD7C2petgqSbT3Bt8a6XtJQVMopVs1JFHTVk1hvwolO1GqtjeaiSJ5IOcna6dgzj6ZxB0Zv0q+F2s+gegbha0kM8C0Jc7+y9sdxHtpkKlNrDZ556OqXre65KgWVPAFcJ6JseZHjj4Fv3vq+eRIS8Pkr39B3lz6ceuNxMbl24foiXrztDea8NBdvrRcU7Lbvrow/cxyjjx4R8831RNtxpSbjSu2a+/DEpcV7a3Wkhmba+xu6+FTQHpq+AFsdLVXuayh7/8TEVvs5ujTcVJgNnPthZFubkJkV9weSiFDqVk0E3nzsg1DZz6Ns3azraQ96y76BpZrt9derboTD+jSu0q5Apf0j5NHaLEEXnxFoHb/V80/aC5XzZItWcWhtojcPpWUJX+yozAdQruC9LrZm1a+8YE3nmYGVSEl7WYWoVdNpy80YVbev6vuSWHs43QY1r9C0c7EBKh2V83ybrhCKhtfj5cTe51NRXBnymORUJ69veqrN32S2rCngkr1voLSwHDNI8zKb3eCAk8dy/j2ns2VNIfPe+QF3tZv+Q/sx7sQxJKckfrRWdF7tpqFZV6G1RpdeA9pN8+F0P+hydPnNqJwXEhGe1R7dNijMShaNSj2v/iuVdhkaL1Q9g/WGW/ei74TUs6zpCN9fVoLlOhycB1s7FNepnQM6+CfE9qPp90FX/tfqpZISfCMwXXY9+JbXfRX4b+Bn7V2ALr8LlXlbC67vo70kIYC10ilKSjms5eAp54CusFbD+AvQhUcQ/ZYCkTqn1t1d2/D/NW8GkhBo+vMzQVegS86Dbl/EbMfgYBbP/T1sEgJWH4ifZv/C2Emj2vTaj17+HGUhkhAAv8/k81e+Ye4b8/HUerDZDZRS+Lx+pl/xLNe9eCmjjxrRpjEJ0Rpx6SPS6XkXgn8Foef0/eCZj/aFWCkQY0rZUDnPQH1TK6sbpsWOyrwX5RjR6HgDI/0aVLevUek3Quo/UBn/RnWfh5F+BUbGLaisB1COPdDur9EVd6Hd39f3SrBqUmKR48b211VXPoLWzRM17VtjTUeEnGowoeYttFka9bWUclh757RYo8ZrbcIGSaNa1fBMKWVNXykXuvolwna2bSaK5eEqBRqPhlQ9TejnHlhCXjs7yuu3jarymqiOq47yuGgVbSxh3qwfw7ZxB2u3XU+tlRz6fSa+QPO0mooabj3uHn7/fnm4hwsRF5KItAXfiiiPWxnbOMJQtp6QNR1sO7B1XYL2/GwNtzd7TDdU6ulWo6yUyfXFjLrmbfSWseiK/0DNO1Z31ZIp6OIT0WZxYLfbKN6QjBbWzSSNgZwXwb4ToZtibcOnYXMj+H5vfrtnAZGfjwe8v7bocirlFFr+J9g4idxWyiqozgxffBqV2tlE1zcmwHVSYBO+UM/FBq7JDTsn6/LAMuBwPwc72vNj9DG0gT47hm5n35rjorV++caIG9uFUzch/+q0t9soIiFaTxKRttCsY2mo46KrIdC+NeiqF9BVT1vLO5vtYNty2l8IxScH2RXWBzWvoEsvD73nQePzuL8NNDyra6nuo361h3cxuuQCtGMc4d+UlLVDbeadLXoOKu1cDMcoVM7LkDyRJqMuKgOVdhmknEbEzp3h6KZdYbV/k7XbcnQPbn6Ldxlmxf2Y5beiq55Bm412nU09w1o5EvWfoQ0y/kXk1TRJkHYjOCcSNmlJnojKfafpPkGtFmUbapWNSr8GlXErKut+rJ/V1j8vw6o7Sru0DeKKrQFD+rLTyEEh27wbhqLvLtuxy95t2wfFlbbt9Samzww0O4vhDt1CREFqRNqCc1+sT+Jh/qBVJjj2DHsabVZZb/LuT+oeBJhgy4esBxo2bGsFXf0smEWEbBPu/tRqI+4IP2esKx8jbItv70IUNdZuuJ75hKxJSbsI5RyNmXo+VD0RxTNwWCtzCKxmyZqGNq8N1G3YIWmwNd3h34KufSfEzr+RGGDrb0VY+7m13Ne3NMrH2hvtW2M1KdOl14D7I+oKYzV+qLgX0q9HpU6xiltzXkBXPWptwEe44Xsb5LyO4RiK6fkGaj8mVEKiMqehXEdbXXIrcqD6JZr8btp2gMx7MRy7BH18q9gHg+dbwvZccY5DZT3UUMPh3BdyX0dXPt7QsVVlQ8opqNRzmi4nVhlW8upfRehRER/KMbLNnlK0rnxyKpePvQl3jadZUyp7ko2rnr6wzZsYDty9P93ycylYWxT54DC0qXHXeHAkx6+uRoityYhIG1BGJqRMIdynT5V2QdgiOqvg9SJwz6Zh/jzwouZfjy6egvatbn2Q1ZGamtms9t9haLMSvN9HOI8dXTsblfUgJA2rv61up1xQqLR/olyBJk9mGVH9GrqORxlNK6+VkYVyjEQ59qj/3ipbd1TODLC3dPWEDZwHomzd0dVvWKuMoum/Alb8yRObbDaoy25slFDWbQxnjSDpin+haz4MPIdUjPSrUT1+hPSbrTiafD8MIBmV/RSGw0p0VOa0Ri3jbY0eY6DSr0W5rA3NlLJjZFyH6v6ttSom4y5U7puovPfbNgkBVMqphE/8TFTaJc3+BlTSEIzs/6F6/Irq/jOq+3fWVOBWPU2UUqjUcwidhNjA6Gbt87QNtG81Zu1czLI7MItOxiw6EbPiIXSY/ZO2360f/5t/J6OPGoEyVH28Iw/bnQfn3cGubTwaAmAYBqffErywuiUyctNJzZS+IyKxZPluG9HaF1ha+BpNl4ZqSD0flXZl2E9F2vMDujjCRnj2nVE5r6KM1PDHNYtNozfvTMQ6B8c4jJzQoxPaX4guiLSttx1cx2Nk3o5p+qH2LXB/hdVxcwdUyvFNeqqYJRdYO+1G0v1njCgabjVmun+EkjOJvDrFBkYOKvcNUCnoLftE8RioHxlK2h2V/UxDDY1vDbrwEMJ2H7UNQOV91HxDSP8GdPVr4PkRlIFy7GMlYYGl0U2O9f6GrvkAdDnKlg+uSShbYjbIarq0tvGImbXEWaVdjkrbtl5C1jVut3q4tPHyXe1dgi7/F3h/DnGEAZl3Ybgmhj1PRUklJZvLyOqWQUZu7Ht4vHb3uzx9w8utqhcxbAYnXzeJM/91UgwiE12dLN9NAKXsqMx/oVPPRtfMArPIKhB1TbSaZkWga96j6YtrEL4/0EVTIPelFm38pZRCGzmBqZlQbBCkqVcTRjaorEB/kFD8KPsO6Oo3oPKRQMdWALtVS7N1nYytFxGfN9nNkhBtVkPtx2j/GpSRDskTmn2flbnBWoYcSfIEq27B1hNd9QLNO5xuTYHqBvbtUK4TwXVk00/67k8JPX0FTfaGsQ9oemZbb1T6FZFjxhpNUIHpqpbQ3t8ChbU2cIxB2fNbfI5msSgFGbeAY3d01XMNU1pJu6NSz0UlH9xG17gZkg9GV78SpKFZ8E0NI9HeJeiikwhf52JC2TWYxnYYztDTP+nZaXHdpOzEa47hkNP34+OnP+eT579kw4pNgdVMCtNvkpadSmVJFYahMBslK4bNoP/gfE64pnPv+SU6BklE2piyD0ClX9byB5plRNXS27/E2sOlpZ8uXZMDtRihlxgr17FhT6GUDZ1yClQ9FuI8CnCgzYLAMY35oPY9tPdnyH0DZWRbj3AdF1j6GYoBqU0/semaWejymwOFpXY0JlT8B518LGT8H0bdiJF/M5GTHGtaoW43ZO1fFXhMuGREo/LeqG+01fzurXc5DnWa0MP9saB9a9ClV4BvcaNbFdo53qoraeGI09aUUlbi7ZoYWIVlRL3xX4uu4dwH5dynzc6py/9N1D1dKqaBs32tNMnpmc0pNx7HKTcex8ZVm5n/3k+4azxsv1s/ho/fje8/+JmX//0Wfy6wVu2lZLiYcM5BTLllMinp0TfhEyJWJBFpL2zbYb15RSqw1Nbqi6SRKMewqJs3qdQz0TXvgrklyDUUOA+DpD2iOM95aPfcwCfexm+0gd1V06+Bin+HeLTfqnepehKVfo11vqTBVgJRO5PmIwg2MHqgUs+ov0XXfoEuu7rRsY0Shtq3ofZtTMe+qLSpYOQFea5BGI1ag6v0IHEEocJMj9kHEnlUxQ622O9BUkf7i9DFJwV28m1yD7g/RZcUQM7LUe3DE414NhULRpulUPM22vsHdTsa49y//vlp/0bwLkWbJVaRdrR8v6HNqhZPj8ZLrwE9mHRp0w65Y44eyZijR1K8qQR3jYfc3jk4nEkhziBE/EmxajuhUo4n6lUeuhxKTrV6eVQ9HdWyW2XkoHJfA8fore5xQspZqKx7o6rsV0YqKuclSL0w8KZdJ1APU/1ihDP4ofq1JkuSVeYdgfM1nm5S1htH7mtNi0ArH4gYI55v0cWnWatUwvYVMSBpGMresAGjtclahD1SHGOaFc424TzIWv0RrkdG8hFWkXOc6OoXwCwm+HPzW7UR7q/iFk8s6ZoPG/rc1L5nJSSlU9GFR2F6l2CWTEUXjEOXXgDlN7TmAm0fdBzk9Mym14AekoSIdkeKVdsRs+IeqHqy5Q9MOQsj4/qoD9e+NYERjSRw7GXVWLSQrv3UWuVjfdXonnC1EY2O6r6g2Zu5NqutLrXaA0k7Nav5sIpAW1Jr4IC0f0Dl/4JFABhWgaNjryb3mCVTwf0lzadWrK6mKufFiMtEtXsuumQq1vdiq72HjB6o3DeCFqDGirllXzDD9UOxgXM8RvaDcYspFrRnIbr4ZIJ3bq373LUtfXkcqB6L2nzKSYjOpiXv3zIi0o6otH+C65SWP7D6WbRvVfTXsfdFJR+GSj6odUmI9ljLU62vtr43ijPYgzZ3U0YKyrkPKvmA4AW+uqKFkXqBNKtNvdqq/sHWG5X9VLMkBEBl3gfOuoTHRv0Mpkq1+mBE0atCOfdD5bwCjn1oGBlxQcqJ1hLaOCYhAERsP+8HszAekcSUrnqC0MmwybYlIVgrkyQJEaJNyV9UO1K3KkB7fwPfb0T/omlD17yJSr86luE1cH+5DZva2SD5sKab5EX90N5EVQRazwD/clTmnZByIri/sYqCbX3AMRKlgufhykhBZT+M9q2A2tloXYWyD7RW17Rgh13l2B2V8xTarLB2pDVyElc7YesJ/nB9aGxxrVmJBa39gZGsljayi5LKid/fmBBdiIyItDNKGaicJxsVjkbzI9Lg3xjLsJryr6V1vzoGYEelTm3VZZWRDc5Dib6FuwJlbXWuVDIk7QZ4wbcEPF8H3eCuyaPtg1BpF1oNx1zHtigJaRp3OsrWM6EFnCrlJMLvUeMP1Cl1ZH5ik4QocOyD6vZJ+NogIUSryIhIO6SMbMh5BbyL0LWzreW6kfZuaVTQGXMqgxaNSqAAPxjdUFn3o5Ja32lSZVyDLvo+yhbuPpTzYGsqqfyOQLM5TX2zOaMnZN6Dcrbt9uztkuskqJkZ2HgxyKqp5CMgqWNvCa+UA23LB/86ot8FeCv2nVCuyWiVYnVqVU5I2h3D2Pa9XYQQwUki0k4ppcCxB8qxB6ZZFljeGuqN11/f1jsukg+C8lsIu0TV6AHZL6E8X4CutXbMde63zctDlW07yH0LXXEv1H5E6ITIBvadwTEaXXYd1L5Lw5tT4L/mFnTJ2ZD7Wqsag3UkykiFnJetnhm1H1D/s1OpkHIGKu3iNt8PJRFUyhR0xbTWnyDlNFTKiW22v7EQIjJZNdMBaN9qdNGkwLLBYD1ADsHIfjiuMZmFx23VGGtrKagePzSbjtBmJdS8g/b+CCiUYxQkH9WqZlraLEVXvxpYFeOnYfTFB/ZdUdlPgS5FFx4e5iw2cO6Pkb11A7bOS5vF4P0DlLVRX2unnNojrT3okvPB8x1NR0WiqS1KCWwlIDPWQmyrlrx/SyLSQWjvUnTplVZr8PpVAQa4joOUs1HmJmtlSNKQZqMOWtdAzfto99eAB5W0W2D/kggt3UPFot3ozaOA8J1BVeZ9KNeRDY/z/Igu+YdVuFn/mdO09gnJfgrliNxQLWg8ZjHUzER7/wTlQiUfAo7RKGVgVjwY6PIafmpLdf9pmzuLxpvWGrw/oms/Be22prySj27VSqjORGsPVL9otes3A7VTjjHWRojVT4V+YNYTGMnj4hIjwJ8/rWTWo5/wxw8rSHLaGXP0SI44/2ByembHLQYhYkUSkU7KeuP5CXzLACfa3s/az8Uzr+Egoxcq/bL6du3a+ye65CwwC2iSwGBDZd6Nch3R8jh8f6MLI+1yaofUszACqwy0fwO6YALWfh5bfzI1rAQi75NWJ0ehmOW3QfVrROp0qrp9GdWeQO2FNoutpM77Cw2bLPoBJyrrHlTyoYkNsB3QWltJr3LUj8zpmllWozOzoOFAow8q819t2jY+khl3zeTpG17BZjfw+6y/B8NQOFOdTPvoJgaP2SlusXQVfr+f377+g8L1xWT3yGTYuMHY7G3TSVg0J5vedVJW3cgIcIxA+/6GouOa71dibrRqIsxKcB2HLjmzUVvvupzT6qegy64Ce19U0tCoY9BmFdoXbhlo/ZE07mqqq18BPAQfHjetaaea1yDtkqhjiYay9bH2ognLEd9i322ktYkuPt9a/QM0He1xo0svg5xXUI49ExFeu6GUatY/RrmOhuTDwbPA6jRr6211141jfcwPHy3k6RteAahPQgBMU1Nb5ebGI+/k5b+nk5oR/caWIrxv3/mBRy57hoK1DRt/5vTKZup/z+CAk+KXgIrgJBHpoHTFfwNJSPApB11xN5GbVCl01bOorPsiX8+sQFf+F6rfBmqjiNAPNTMxa96EpF3BuzRkrBYTXTsH1caJCMnHQMW9YQ6wWRu1qQ60KsLzHfh+DXGnNeKlqx5HOR6PZ1QdhlJ2cO4dk3P7fX6+en0e7z8+hw0rN5GencbBU/bn8PMOqt+V943/zsKwGZj+5gmyNjVVZdV8+uJcjrnosJjE2NXMm/Ujtx53T7PbizeWcOcpD6C15sCTxyYgMlFHqrI6IG2WgXsO4d/YvVDzFpF6R+D+PIrrVaGLTwlMcUSThASYG6y24u65EdqL110o3DbsraNseaj0q0LcawMjG5V2cZtfN5Z07RzCf4awGntZO+CKePG4vdx4xJ1MO+0hlnz7B0UbSvh7yVqevuFlzt/tKjb+tRmtNYvnLg2ahNRRKBZ98VscI++8TNNk+hXPWV+EKEJ47Mrn8Pti1ARPREUSkY7ILCDyCgAbmNVE7Kego9j+vPpF8C0nYi+TkL9O0fyR2wINx7YKT2u050fMigcxK+5Hu7+M2IisWWSp56Iy7gSjV6NbDXAeENjzpWeLzpdwOoqfKzomiZ0I7cVbX+fnz6yVZKbZ8PPRpqZkcym3HXeP9ftsRv7ZtePSvQ7l9/nL2bRqS9g/l5LNZSz8XBK/RJKpmTaizSqofR/tXQIqCeXcHxxjQ7YR3yYqK4qDTLDng2cDoZMWw5o2iUBXvxrmHAAKbP3AyAPvgihiC8aPSmm6z472b0SXXBDYoM8qyNRVPqsVedajqKSdoz67SjkeXMeC73frjdzWP/77vbQRZR+EjpSIGHnN99cRMeOucTNr+ichkwy/z2TlL6v5ff5ydh2zE0u/+zPMqIhiyD7R/26L0Io3RrcVRbTHidiQEZE2oN1fowvGosv/D2rehOpX0SXnoouORseg9bqy5YFjbyL++NKujHAmE5VyetgjtDYblkCGPgrsA8Hej+jarxvN/z/1oibLd7WuQRdPCawQAmtUJbDyxb8RXTwF7d8SxbUaKGWgkgajHCM7bBICWAlV2J+9gUo5tV01KNPaj3bPQ9e8bf29RDMS14GsXrqO6vKasMcYNoPFX//OcVccGTIJUUrhdDk49KwDYhFml5PbO7ql0NEeJ2JDEpFtpL1/BHpj1K1e8VH/hulbiS4+IyZz9SrtShoaeAWRcjqGYxgq447AMY0ThMCPPflYSD4yyIMbXUcZoCJV79sCbd+jfeNz1p0dkvZAZT2CkX5Z00Nq3gf/GoJP6/hBVwRW4nQ9ypaHyrgt8NXWf8IGJA2F1LPjHVZIunY2umAcuuRMdNl16JJz0AX7omveSXRobSbapE8pxT4T9+KkaycCYLM3/PwMm4Hdaee2d66pL2wV22aXvXek1/Y9CPfjye6Zxe4HdO7Oyu2dJCLbSFc9jTUBGWxI1g/+v6H20za/rnLsjsp+0mqlbt0S+G8SpJ6PSr/WujXlOFTOy+A8wLoPw+o6mnkPKnNadC+gyUcTfqTDj3IdgXLsTXT1IG7rfNnPY+S+ajUg24qu/YDwiY0JNe9Gca3OSaVMRmU/DUmNluiqLEi9AJXzQrvplqprP0OXXgLmVqNXZjG67Bp0zczEBNbG+g3OJy07Newxpt9k9wMGo5TinGmncvenN7P3kSPI7Z1Dr+17MOnSw3n6t/vZ8+DmtVKidZRSXPjAWYAKmYxc9MBZ0k8kwaSh2TYyNw0Dwg3JGpB8GEbWAzG5vtZ+q6GZ728w0qwCTCMrzPG6xUP2Vov5owPFj1sPKdusNuE5MwAfumCc1Z8hYjGtYdVp5H0UNB6zcFKjPhkhqEyMHj9G/Tw6K22WWz8bI9tamtpOaG2iCw8G/3pCVguqbFT3b1AqKa6xxcKLt73Bi7e/EbTQ1GY32HH4QB767s4ERCbmv/8TD1/6NJv/bmhkl9cnl6n/PYP9J49OYGSdlzQ0ixPrBSfSclYTzPBzx9tCKRs497X+RXV8S5OQFeiql6ypF13Xk8SG9cZigmOMtaOuMgAHZD+FLj4DdDnhV3aYVrt63+Kgq2WwDwTfH4QeYTHAPqBFz6Wzardb03t/DeyEG4YuAc+34BwXl5Bi6ZQbj+Wvxav55u3v6/uEKKXQaHr0787Nb4ZaRi5ibe8jh7PX4XuwdN4yq7NqzyyGjN0Zm01GQtoDSUS2gVIKbds+sP9LqDddGyQNimdYbUbXvIcuu5qG9uFgzeb5wXkoKu1SVNIOTR6jknaFbp+gy28L7I4bgX998ETEdQLUzgrzQBOVcnJ0T0QkRuM26uH4wzXd6zhsdhv/9/qV/PjRQt5/Yg7r/9xIZrcMDjp1Pw46bV9cqR2oaV4nZBgGQ8bukugwRBCSiGwjlXIquuJfYY4wUa4T4xZPW9G+VYEkZOsplsDX7tmQdkHQxyojB1yT0NEkIqp5tbq1adkL4R/n2BeSj4p8fpE4RpT7Btl6RD6mgzAMg1FHDGfUEcMTHYoQHYYUq26rlBPBsQ/NCyutb61KvxZl7xv3sLaVtSIl3DSOYU3ZhOIYE7nfiZFn7Z2z9bXL7wR3mALfpOGo7Ontqh5CBJG0m9VfJtzvkZEHDpmjF6Irk0RkGymVhMp+DJV2VdNPgElDUVmPotrRMsoW8cwj/AoYf9Ndf7eilAOVHr6PiUq/ulkyoc1iqHmdsMWuvqWgw++mKxJPKYXKuBkrEQmejKj0myShFKKLk1eANqCUA9LOh9RzrZ1uVVL7LSCMWjRFreGPUSkngfahK+8N9FkxABNUmjVS5JrU/EHuedT3YQlF11gdXJ37RRGjSCTl3Beyn0CX/wv8jXZtNnqhMm5AJR+auOCEEO2CJCJtSCkDbLmJDqNtOMaAbyWhR0Vs4Gy6fbY2S6yeKbrcasPuPACVeprVCdT9uVW8aHSH5IPC7HYbZfO3QJM4rf2gK0G5rIRQtDvKuR/kzQbvL2BusqZjkvaMzfYHQogORxIREZRKOQVd/WKYI0xUymlAoF9E5QNQ9RTWaEbdyEcWZP4blTweXOE7uNazR977BhTa2A5dcY+1I7AuB2xo5yGotKnWyh3RriilwLF7osMQQrRD8pFEBKXs/VGZ/8XqGdJ4rb0NMFAZ0+o3ndOV90HVYzRMqQTqO3QZuvQStPub6K+btDPYhxD6V9MGSXtD2WVQ9XQgCQHwg3sOumgy2v1d1NcTQgiRWJKIiJCU63BU3oeQchrYBoJte0g5EZX7HirlWAC0vwiqnglxBqu3iq64L6rraf8WzNKrA43MghWr2sDoBkYu+NcGOcYP+NFlV3a6TdWEEKKzkqkZEZayD0Bl3Njsdu39A13zBri/I3xxqQbfb2jfmrDLmLW/EF10ApibCV6XkgypUyD5RCg6PMQxYHWyLbJqUqQQUggh2j1JRESLaK3RlfcE6kFsRLfJHWjfX+ETkcqHwyQhAD5U6rng34iOWNBqB9+fgCQiQgjR3snUjGiZmlcDSQhEm4QA4Pkp5F1au6Hm7Qjn80PNLIhqV1kTkHbaQgjREUgiIqJmrY55vJUPLgp9n1lE5M0DbWj/arD1B1tfwvcwMSH5oBaHKIQQIv5imojccccdjBkzhpSUFLKysmJ5KREP/lVgbmzFAw1QaaHvVmlEbqCmQWVY3TrTLib0JoMGOA9B2bdvRZxCCCHiLaaJiMfjYfLkyVxwQfDN0UQH0+qVKCYq+fCQ9yojw2qgRrgtuf2o5COs410TUWl1uwIbNFli7BiDyry7lXEKIYSIt5gWq952220APPfcc7G8jIgXez+rRkPXtOBBBjj2hqRhYY9SaZeii+djJRdbj3YY4ByPStqh0fHnWU3Sat5G+9aAkWElO0m7W82zhBBCdAjtatWM2+3G7XbXf11eXh7maBFvSrnQrhOh+gXCbkpXv8mZCc79UJn3oZSy2rG756I9X4H2oZKGQvKRKCMV5dgDsh5Fl10LuhTrV9MEtHVM5h3Nr2LrBWkXRbUrjhBCiPapXSUi06ZNqx9FEe2TSrsM7fkJfL8FbglVq2GC6wRUxr+sJMS3Dl1yjlVnEvi10zWvQ8VdkPUQyrkvKvkAcH5j7VfjXwkqJVDvEXrZrxBCiI6txTUit956q1UwGObfggULWhXM9ddfT1lZWf2/tWvXtuo8InaUkYrKfRmVfi3Y8kMcFUhOal6HmtfR2o0uOR38awL3+6hvgqar0SVT0d7l1vmVw+romnYJKvUcSUKEEKKTa/GIyMUXX8xJJ50U9pj+/fu3Khin04nT6WzVY0X8KJUMqWeDY2900cRwR6KrHgec4F8X4hgNmOiqZ1FZd7Z5rEIIIdq3FicieXl55OXlxSIW0dG4vyJ8d1VtJSC171K/I29QfnB/BERORCq9tSyv2IihDHbK6EWyzdGayIUQQrQTMa0RWbNmDcXFxaxZswa/38+iRYsAGDRoEGlpYfpKiA5Baw+R+38Auprwxa2Adoe9u9rn5uE/P2bWup/wmNa0TorNwQn9RnP+oIOxG+GW/gohhGivYpqI3HzzzTz//PP1X++xxx4AfPHFF4wbNy6WlxZxoJIGo8NueIe13DdpN/D+SuiREwX2QSFP4TF9XLLgWZaUrsVsVBxb7ffw/F9z+buygLv2OAVDSaNgIYToaGL6yv3cc89Zm6Rt9U+SkE7COQ6M7oT+NTKslTMppxB+RESjUk4Lee+H6xeyuHRNkySk4ZGaL7csZX7h8hYELoQQor2Qj5Ci1ZSyo7IeBuWkeVdUBfZdUGmXoewDUOlXB27f+ldOgWMcuI4NeZ231/6ACjMFZEPx7rrWrdQSQgiRWJKIiG2iHLujct8F12Sr7weAbTtU+tWo3FdQhlULpFLPRWU9AklDGx5s9EKlX4PKfgSlQs8SbqwpQYfsVwJ+NOuqw2yqJ4QQot1qVw3NRMek7P1RmbdD5u1orUO2WFfJh6CSD0GblYAHVHZU7dgzk1Io81aHvN9AkeOQ4mchhOiIJBERbSqaxKJulCSc7wr+5LXV8/itbG39KplQTDSHb7dH1DEKIYRoPyQREe3Ow8s+5oVVc7Gh8IeZkgGwKYMBqd05qOfQsMcJIYRon6RGRLQrX2/5nRdWzQUImYTUbakHMCJnex7d6xwchuTUQgjREcmrt2hXXv37WwxU0KW6YCUge+ftwNhuOzMidyAD0rrHN0AhhBBtShIR0a4s3qpp2dY04Ncmk/uNbnafqU1+LFrJxxt+ocxbTS9XFkf3GcFOGb1jGLEQQohtIYmIaFeMKIpdbap5O/dqn5t//vwiC4r/wqYM/NrEpgzeWDOf4/L34updj5bOq0II0Q7JK7NosUpfLbPWLeCpFZ/x1prvKfWEXlrbUnvn7YAtTMKggFF5zdvB//u3t/m5eBVgjZg0/u9ba3/gxVVft1mMQggh2o6MiIgWeX31d/xv2Ue4TR82ZWBqk//+/h5nDzyAcwYeGNXy3XBO6T+WLzYvCXqfgSLF7uTI7YY3uX19dTGfbloc9rwvrfqaU/rvQ5IUtQohRLsiIyIiau+t+4l7f38Pd6Cvh19b1Rw+bfLEis/qV7tsi2HZ/bhh8CQUqsnIiELhsjt4YPiZZCS5mjzmu8I/I+4BXOat5o/yDdscnxBCiLYlHw9FVPzaZPry2WGPeWblF5zQdzQuu2ObrjUxfyR75Azg7TXfs7h0DUmGjX267czRfYaT5UhtdrzP9GNN2oTvOeKN0BhNCCFE/EkiIqKyuHQNhe6KsMfU+D18V/gnB/Ycss3X65eaxxW7HBHVsTtl9A67Fw1Yjc+2T+uxzXEJIYRoW5KICDbXlvHWmvnM3vgrNT4P26f34Pi+ozigx+D6lSYV3pqozlXe6LhKby1fbl5CkaeS7skZ7N99V1LszjaPf/fs/vRP7caaqsKgS39tyuDQXrsFHU0RQgiRWJKIdHFLy9Zx0Q9PU+P31L+JLyxexU/Ff3FIz6HcPuxEbMpgu5ScqM7XJyUHrTUv//0Njy+fU1/U6tcmLpuDS3Y6jOP77t2mz0EpxZ27n8w/vn+Car+nfrUMWAWu+Sm5XL7zkW16TSGEEG1DilW7MK/p48qfXmiShAD1/z9n02JeX/0dANun9WDXzD4YIcpCFYperiz2zBnA66u/46HAyhpoWEZb4/dw99JZvLfupzZ/LoPSe/LyPpcyue/epNuTUUB3ZwbnDTqIZ/a+gCxHSptfUwghxLZTWuvwk+sJVF5eTmZmJmVlZWRkZCQ6nE5n9sZfuOmX18Ie0zM5i3f2/yeGMvi9bD3nf/8EXu3DbPRrY6BQSvHgiDPZLasfE764kyqfO+Q5cx1pvDfuWuxG88ZkbUVrvc1LiYUQQrROS96/ZUSkC/u1ZA32CN1GN9WWUuypAmCXzO14eu+p7JUzqMm4yNCsvjy213nslTuIH4pWhE1CAIo8lfxSsnpbww9LkhAhhOgYpEakC4umnTrQZDpmx4xePDTyLApqyylwl5PjSKOnK6v+/vIou6yWe9uuG6sQQoiOS0ZEurARuQPxNSrs3JoC+qd2IzvIapNuyRnsmtmnSRIC0DvKotZojxNCCNG5SSLShe3TbSe2c+WE3NtFA6cN2LdF0xy7Z/djO1c2KkRRq4FiUHpPdkzv1ZqQhRBCdDKSiHRhNmXwwPAzyHakNkkb6hKTU/qP5ait9nWJxFAG1w+ehKFUsxU2RqBt+3W7TpQaDiGEEICsmhFYu+l+uH4hn25aTJWvlh3Se3Js/ih2y+7X6nP+XLyKh5d9zG9la+tv2z27P5ftNIHBWfltEbYQQoh2qiXv35KIiJhaV11EkbuSbs4MeqdkJzocIYQQcdCS929ZNSNiqk9KLn1SchMdhhBCiHZKakSEEEIIkTAyIiJEQKWvlo/WL+TH4pX4TZPdsvtxdJ/hZDvSEh2aEEJ0WpKICAH8VrqWyxY8S6WvFrCWLn9TsIwnV3zGXbufwtjuOyc2QCGE6KRkakZ0eSWeSi5d8AxVPjca6rf/02i8po9rFr7M35VbEhmiEEJ0WpKIiC5v1rqfqPI13YG4jpWYaF5f8138AxNCiC5AEhHR5c3d8js6SBJSx69Nvtq8NI4RCSFE1yGJiOjyvKYvimP8cYhECCG6HklERJc3JCs/5H47YLW83zWzTxwjEkKIrkMSEdHlHZc/Cn+YXYj92uTEfmPiGJEQQnQdkoiILm9gek+u3PkIAGyNNuqr27Tv1P5j2Ttvh4TEJoQQnZ30ERECOKn/Pmyf3oNXVn3DD0Ur0WiGZOZzSv99GNdjsOwWLIQQMSKJiBABe+UOYq/cQQBorSX5EEKIOJCpGSGCkCRECCHiQxIRIYQQQiSMJCJCCCGESBhJRIQQQgiRMJKICCGEECJhJBERQgghRMLELBH5+++/OeeccxgwYAAul4uBAwdyyy234PF4YnVJ0YZ8pp/11cVsqilF69AbwgkhhBDbImZ9RP744w9M0+Txxx9n0KBB/Pbbb5x33nlUVVVx7733xuqyYhv5TD8vrvqaGau/pcRTBcB2rhxO334/JvYZKctahRBCtCml4/hx95577mH69On89ddfUR1fXl5OZmYmZWVlZGRkxDg64TP9XLPwZb4tWIam4ddCARo4qd8YrtzlyITFJ4QQomNoyft3XGtEysrKyMnJieclRQvM3vgr3xT80SQJAeq/mrF6HotL18Q/MCGEEJ1W3BKRlStX8r///Y+pU6eGPMbtdlNeXt7kn4ift9bMr9/oLRibMnh7zQ9xjEgIIURn1+JE5NZbb0UpFfbfggULmjxmw4YNHHbYYUyePJlzzz035LmnTZtGZmZm/b/8/PyWPyPRan9XFWASeqbOr01WVW6JY0RCCCE6uxbXiBQWFlJYWBj2mP79+5OcnAxYScgBBxzAqFGjeO655zCM0LmP2+3G7XbXf11eXk5+fr7UiMTJUV/+h821ZSHvVyhG5G7PIyPPiWNUQgghOpqW1Ii0eNVMXl4eeXl5UR27fv16DjjgAIYPH86zzz4bNgkBcDqdOJ3OloYk2sj4XsN4edXXIUdFNJpDeu4W56iEEEJ0ZjGrEdmwYQPjxo0jPz+fe++9l4KCAjZt2sSmTZtidUmxjU7ouzcuuyNonYhNGfRyZXNor2EJiEwIIURnFbM+IrNnz2bFihWsWLGCPn36NLlPGmS1Tz1cWUwfeS5X/fwCBe4K7MpAY9WG9E/txn3DT8dldyQ6TCGEEJ1IXPuItJT0EUkMn+nn64I/WFyyBpsyGJk3kJE5A6WZmRBCiKjEtEZEdH52w8YBPQZzQI/BiQ5FCCFEJyeb3gkhhBAiYSQREUIIIUTCSCIihBBCiISRREQIIYQQCSOJiBBCCCESRhIRIYQQQiSMJCJCCCGESBhJRIQQQgiRMJKICCGEECJhJBERQgghRMJIIiKEEEKIhJFERAghhBAJI4mIEEIIIRJGEhEhhBBCJIwkIkIIIYRIGElEhBBCCJEwkogIIYQQImEkERFCCCFEwkgiIoQQQoiEkURECCGEEAkjiYgQQgghEkYSESGEEEIkjCQiQgghhEgYSUSEEEIIkTCSiAghhBAiYSQREUIIIUTCSCIihBBCiISRREQIIYQQCSOJiBBCCCESxp7oAIRoS7V+D98XrqDCV0t+Si67ZfVFKZXosIQQQoQgiYjoFLTWvPT31zyz4guq/O762/um5HHT0GPZPbt/4oITQggRkkzNiE7hmZVf8L9lHzdJQgDWVRdx0Q9Ps7RsXYIiE0IIEY4kIqLDK/VU8dTKz4PeZ6Lxa5NH/vwkzlEJIYSIhiQiosP7dNNiTG2GvN9E82PRSgpqy+MYlRBCiGhIIiI6vCJ3BTYV+Ve5xFMZh2iEEEK0hCQiosPLc2bgDzMiAqCAHGd6fAISQggRNUlERId3cM+h2JUt5P02pRidtyN5kogIIUS7I4mI6PAyHSlM3fGQoPcZKOzKzoU7HhrnqIQQQkRD+oiITmHKgP1w2Rw8sfxTSr3V9bfvkN6LG4ZMYseMXgmMTgghRCiSiIhO4/i+ezOxz0h+LllFlbeW7VJyJQERQoh2ThIR0anYDRt75Q5KdBhCCCGiJDUiQgghhEgYSUSEEEIIkTCSiAghhBAiYWKaiBx99NH07duX5ORkevXqxZQpU9iwYUMsLymEEEKIDiSmicgBBxzA66+/zrJly3jrrbdYuXIlxx9/fCwvKYQQQogORGmtdbwuNmvWLCZOnIjb7SYpKSni8eXl5WRmZlJWVkZGRkYcIhRCCCHEtmrJ+3fclu8WFxfz8ssvM2bMmJBJiNvtxu12139dXi67pQohhBCdWcyLVa+99lpSU1PJzc1lzZo1vPvuuyGPnTZtGpmZmfX/8vPzYx2eEEIIIRKoxYnIrbfeilIq7L8FCxbUH3/11VezcOFCZs+ejc1m4/TTTyfUbND1119PWVlZ/b+1a9e2/pkJIYQQot1rcY1IYWEhhYWFYY/p378/ycnJzW5ft24d+fn5zJs3j9GjR0e8VllZGVlZWaxdu1ZqRIQQQogOory8nPz8fEpLS8nMzAx7bItrRPLy8sjLy2tVYHU5T+M6kHAqKioAZIpGCCGE6IAqKioiJiIxWzXzww8/8MMPPzB27Fiys7P566+/uPnmm9m4cSNLlizB6XRGPIdpmmzYsIH09HSUUm0SV12WJqMsiSM/g8SS739iyfc/8eRnEHtaayoqKujduzeGEb4KJGarZlwuF2+//Ta33HILVVVV9OrVi8MOO4wZM2ZElYQAGIZBnz59YhJfRkaG/AImmPwMEku+/4kl3//Ek59BbEUaCakTs0Rk6NChfP7557E6vRBCCCE6AdlrRgghhBAJ0+USEafTyS233BL19JBoe/IzSCz5/ieWfP8TT34G7UtcW7wLIYQQQjTW5UZEhBBCCNF+SCIihBBCiISRREQIIYQQCSOJiBBCCCESpssnInfccQdjxowhJSWFrKysRIfT6T366KMMGDCA5ORkhg8fztdff53okLqMuXPnctRRR9G7d2+UUrzzzjuJDqlLmTZtGiNHjiQ9PZ3u3bszceJEli1bluiwuozp06ez22671TcxGz16NB999FGiwxJIIoLH42Hy5MlccMEFiQ6l03vttde4/PLLufHGG1m4cCH77rsvEyZMYM2aNYkOrUuoqqpi2LBhPPzww4kOpUv66quvuOiii5g/fz5z5szB5/Mxfvx4qqqqEh1al9CnTx/uuusuFixYwIIFCzjwwAM55phjWLJkSaJD6/Jk+W7Ac889x+WXX05paWmiQ+m0Ro0axZ577sn06dPrb9tll12YOHEi06ZNS2BkXY9SipkzZzJx4sREh9JlFRQU0L17d7766iv222+/RIfTJeXk5HDPPfdwzjnnJDqULq3Lj4iI+PB4PPz000+MHz++ye3jx49n3rx5CYpKiMQpKysDrDdDEV9+v58ZM2ZQVVXF6NGjEx1OlxezvWaEaKywsBC/30+PHj2a3N6jRw82bdqUoKiESAytNVdeeSVjx45lyJAhiQ6ny1i8eDGjR4+mtraWtLQ0Zs6cya677prosLq8Tjkicuutt6KUCvtvwYIFiQ6zS1JKNflaa93sNiE6u4svvphff/2VV199NdGhdCk77bQTixYtYv78+VxwwQWcccYZLF26NNFhdXmdckTk4osv5qSTTgp7TP/+/eMTjAAgLy8Pm83WbPRjy5YtzUZJhOjMLrnkEmbNmsXcuXPp06dPosPpUhwOB4MGDQJgxIgR/Pjjjzz44IM8/vjjCY6sa+uUiUheXh55eXmJDkM04nA4GD58OHPmzGHSpEn1t8+ZM4djjjkmgZEJER9aay655BJmzpzJl19+yYABAxIdUpentcbtdic6jC6vUyYiLbFmzRqKi4tZs2YNfr+fRYsWATBo0CDS0tISG1wnc+WVVzJlyhRGjBjB6NGjeeKJJ1izZg1Tp05NdGhdQmVlJStWrKj/etWqVSxatIicnBz69u2bwMi6hosuuohXXnmFd999l/T09PrRwczMTFwuV4Kj6/xuuOEGJkyYQH5+PhUVFcyYMYMvv/ySjz/+ONGhCd3FnXHGGRpo9u+LL75IdGid0iOPPKL79eunHQ6H3nPPPfVXX32V6JC6jC+++CLo7/oZZ5yR6NC6hGDfe0A/++yziQ6tSzj77LPrX3u6deumDzroID179uxEhyW01tJHRAghhBAJ0ylXzQghhBCiY5BERAghhBAJI4mIEEIIIRJGEhEhhBBCJIwkIkIIIYRIGElEhBBCCJEwkogIIYQQImEkERFCCCFEwkgiIoQQQoiEkURECCGEEAkjiYgQQgghEkYSESGEEEIkzP8DffiqAg4RlKAAAAAASUVORK5CYII=",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"clusters = hclust.fclusterdata(randpts,4,'maxclust',method='complete',metric='cityblock')\n",
"plt.scatter(randpts[:,0],randpts[:,1],c=clusters);"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"import sklearn\n",
"import sklearn.datasets\n",
"roll,_=sklearn.datasets.make_swiss_roll()\n",
"roll = roll[:,[0,2]]\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"clusters=hclust.fclusterdata(roll,5,'maxclust',method='single')\n",
"plt.scatter(roll[:,0],roll[:,1],c=clusters)\n",
"plt.savefig('imgs/roll.png',bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%html\n",
"\n",
"
\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# `leaves_list`\n",
"\n",
"A hierarchical cluster imposes an order on the leaves. You can retrieve this ordering from the linkage matrix with `leaves_list`"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 88, 93, 86, 66, 173, 67, 12, 43, 53, 5, 40, 61, 49,\n",
" 79, 30, 13, 41, 68, 45, 108, 158, 11, 44, 81, 78, 197,\n",
" 107, 182, 185, 190, 148, 104, 199, 101, 105, 152, 165, 117, 144,\n",
" 142, 132, 139, 56, 65, 180, 135, 141, 32, 89, 42, 10, 74,\n",
" 128, 7, 51, 47, 52, 24, 69, 28, 33, 77, 91, 23, 36,\n",
" 102, 133, 159, 18, 6, 99, 25, 95, 160, 193, 64, 26, 127,\n",
" 115, 147, 110, 154, 196, 162, 183, 121, 84, 38, 83, 138, 187,\n",
" 122, 198, 22, 100, 155, 120, 8, 194, 125, 184, 192, 168, 191,\n",
" 169, 114, 126, 151, 1, 118, 21, 85, 97, 98, 92, 0, 29,\n",
" 27, 156, 186, 15, 2, 20, 87, 3, 174, 90, 172, 140, 17,\n",
" 130, 131, 63, 82, 14, 76, 80, 34, 153, 16, 73, 71, 37,\n",
" 62, 39, 9, 176, 57, 4, 46, 72, 35, 94, 19, 195, 48,\n",
" 59, 70, 31, 55, 137, 157, 60, 96, 58, 75, 54, 50, 188,\n",
" 134, 149, 171, 116, 119, 189, 175, 143, 106, 111, 163, 112, 181,\n",
" 129, 170, 177, 103, 123, 124, 161, 146, 166, 179, 167, 136, 164,\n",
" 113, 178, 145, 109, 150], dtype=int32)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hclust.leaves_list(linkage_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16,3))\n",
"hclust.dendrogram(linkage_matrix);\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%html\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Project: Cluster Expression Data\n",
"\n",
" * Download [https://asinansaglam.github.io/python_bio_2022/files/Spellman.csv](https://asinansaglam.github.io/python_bio_2022/files/Spellman.csv)\n",
" * read the expression data into a numpy array (hint: `np.genfromtxt`)\n",
" * cluster it with the default parameters\n",
" * retrieve the leaves\n",
" * reorder the orginal data according the the leaf order\n",
" * display the result as a heatmap (`plt.matshow`)\n",
" * try different distance metrics and linkages - what gives the best result?"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"--2023-10-16 16:47:44-- https://asinansaglam.github.io/python_bio_2022/files/Spellman.csv\n",
"Resolving asinansaglam.github.io (asinansaglam.github.io)... 185.199.108.153, 185.199.109.153, 185.199.110.153, ...\n",
"Connecting to asinansaglam.github.io (asinansaglam.github.io)|185.199.108.153|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 609183 (595K) [text/csv]\n",
"Saving to: 'Spellman.csv.3'\n",
"\n",
" 0K .......... .......... .......... .......... .......... 8% 9.22M 0s\n",
" 50K .......... .......... .......... .......... .......... 16% 20.2M 0s\n",
" 100K .......... .......... .......... .......... .......... 25% 16.4M 0s\n",
" 150K .......... .......... .......... .......... .......... 33% 20.8M 0s\n",
" 200K .......... .......... .......... .......... .......... 42% 64.9M 0s\n",
" 250K .......... .......... .......... .......... .......... 50% 16.4M 0s\n",
" 300K .......... .......... .......... .......... .......... 58% 55.0M 0s\n",
" 350K .......... .......... .......... .......... .......... 67% 65.5M 0s\n",
" 400K .......... .......... .......... .......... .......... 75% 8.90M 0s\n",
" 450K .......... .......... .......... .......... .......... 84% 157M 0s\n",
" 500K .......... .......... .......... .......... .......... 92% 139M 0s\n",
" 550K .......... .......... .......... .......... .... 100% 194M=0.02s\n",
"\n",
"2023-10-16 16:47:44 (23.5 MB/s) - 'Spellman.csv.3' saved [609183/609183]\n",
"\n"
]
}
],
"source": [
"!wget https://asinansaglam.github.io/python_bio_2022/files/Spellman.csv"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.pylab import cm\n",
"import numpy as np\n",
"import scipy.cluster.hierarchy as hclust\n",
"import matplotlib.pyplot as plt\n",
"data = np.genfromtxt('Spellman.csv',skip_header=1,delimiter=',')[:,1:]\n",
"Z = hclust.linkage(data,method='complete')\n",
"leaves = hclust.leaves_list(Z)\n",
"ordered = data[leaves]\n",
"plt.matshow(ordered,aspect=0.01,cmap=cm.seismic);"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.matshow(data,aspect=0.01,cmap=cm.seismic);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 4
}