Package csb :: Package statistics |
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# Source Code for Package csb.statistics

      1  """
      2  Statistics root package.
      3
      4  This package contains a number of common statistical utilities. Sub-packages
      5  provide more specialized APIs, for example L{csb.statistics.pdf} defines the
      6  probability density object model.
      7  """
      8
      9 -class Cumulative(object):
     10
     11      total_mem = 1e8
     12
     13 -    def __init__(self, data):
     14
     15          self.data = data
     16
     17 -    def __call__(self, x, nchunks=None):
     18
     19          from numpy import greater, reshape, concatenate
     20
     21          c = []
     22          x = reshape(x, (-1,))
     23
     24          if nchunks is None:
     25
     26              total_size = len(x) * len(self.data)
     27              nchunks = total_size / self.total_mem + int(total_size % self.total_mem != 0)
     28
     29          size = int(len(x) / nchunks + int(len(x) % nchunks != 0))
     30
     31          while len(x):
     32
     33              y = x[:size]
     34              x = x[size:]
     35
     36              c = concatenate((c, greater.outer(y, self.data).sum(1) / float(len(self.data))))
     37
     38          return c
     39
     40 -    def cumulative_density(self, x, nchunks=None):
     41          return 1 - self.__call__(x, nchunks)
     42
     43 -def geometric_mean(x, axis=None):
     44      """
     45      @param x:
     46      @param axis: compute the geometric mean along this axis
     47
     48      @return: geometric mean of x
     49      """
     50      from numpy import exp, log, clip, mean
     51
     52      return exp(mean(log(clip(x, 1e-300, 1e300)), axis))
     53
     54 -def harmonic_mean(x, axis=None):
     55      """
     56      @param x:
     57      @param axis: compute the harmonic mean along this axis
     58
     59      @return: harmonic mean of x
     60      """
     61      from numpy import mean
     62
     63      return 1 / mean(1 / x, axis)
     64
     65 -def kurtosis(x, axis=None):
     66      """
     67      @param x: random variables
     68      @param axis: compute the kurtosis along this axis
     69
     70      @return: Sample kurtosis of x
     71      """
     72      from numpy import mean, std
     73
     74      m = x.mean(axis)
     75      a = mean((x - m) ** 4, axis)
     76      s = std(x, axis)
     77
     78      return a / s ** 4 - 3
     79
     80 -def skewness(x, axis=None):
     81      """
     82      @param x: random variables
     83      @param axis: compute the skewness along this axis
     84
     85      @return: Sample skewness of x
     86      """
     87      from numpy import mean, std
     88
     89      s = std(x)
     90      return mean((x - x.mean()) ** 3, axis) / s ** 3
     91
     92 -def autocorrelation(x, n):
     93      """
     94      auto-correlation of a times series
     95
     96      @param x: time series
     97      @type x: numpy.array
     98      @param n: Maximal lag for which to compute the auto-correlation
     99      @type n: int
    100      """
    101      from numpy import array, mean, std
    102      x = x - x.mean()
    103      return array([mean(x[i:] * x[:len(x) - i]) for i in range(n)]) / std(x)**2
    104
    105 -def probabilistic_and(p, axis=0):
    106      """
    107      Probabilistic version of AND
    108      """
    109      from numpy import array, multiply
    110      return multiply.reduce(array(p), axis=axis)
    111
    112 -def probabilistic_or(p, axis=0):
    113      """
    114      Probabilistic version of OR
    115      """
    116      from numpy import array
    117      return 1 - probabilistic_and(1 - array(p), axis)
    118
    119 -def probabilistic_xor(p, axis=0):
    120      """
    121      Probabilistic version of XOR.
    122      Works only for axis=0.
    123      """
    124      from numpy import array
    125
    126      p = array(p)
    127      p_not = 1 - p
    128
    129      P = []
    130
    131      for i in range(p.shape[axis]):
    132          x = p_not * 1
    133          x[i] = p[i]
    134          P.append(probabilistic_and(x, 0))
    135
    136      return probabilistic_or(P, 0)
    137
    138 -def principal_coordinates(D, nd=None):
    139      """
    140      Reconstruction of a multidimensional configuration that
    141      optimally reproduces the input distance matrix.
    142
    143      See: Gower, J (1966)
    144      """
    145      from numpy import clip, sqrt, take, argsort, sort
    146      from csb.numeric import reverse
    147      from scipy.linalg import eigh
    148
    149      ## calculate centered similarity matrix
    150
    151      B = -clip(D, 1e-150, 1e150) ** 2 / 2.
    152
    153      b = B.mean(0)
    154
    155      B = B - b
    156      B = (B.T - b).T
    157      B += b.mean()
    158
    159      ## calculate spectral decomposition
    160
    161      v, U = eigh(B)
    162      v = v.real
    163      U = U.real
    164
    165      U = take(U, argsort(v), 1)
    166      v = sort(v)
    167
    168      U = reverse(U, 1)
    169      v = reverse(v)
    170
    171      if nd is None: nd = len(v)
    172
    173      X = U[:, :nd] * sqrt(clip(v[:nd], 0., 1e300))
    174
    175      return X
    176
    177 -def entropy(p):
    178      """
    179      Calculate the entropy of p.
    180      @return: entropy of p
    181      """
    182      from csb.numeric import log
    183      from numpy import sum
    184
    185      return -sum(p * log(p))
    186
    187 -def histogram2D(x, nbins=100, axes=None, nbatch=1000, normalize=True):
    188      """
    189      Non-greedy two-dimensional histogram.
    190
    191      @param x: input array of rank two
    192      @type x: numpy array
    193      @param nbins: number of bins
    194      @type nbins: integer
    195      @param axes: x- and y-axes used for binning the data (if provided this will be used instead of <nbins>)
    196      @type axes: tuple of two one-dimensional numpy arrays
    197      @param nbatch: size of batch that is used to sort the data into the 2D grid
    198      @type nbatch: integer
    199      @param normalize: specifies whether histogram should be normalized
    200      @type normalize: boolean
    201
    202      @return: 2-rank array storing histogram, tuple of x- and y-axis
    203      """
    204      from numpy import linspace, zeros, argmin, fabs, subtract, transpose
    205
    206      if axes is None:
    207
    208          lower, upper = x.min(0), x.max(0)
    209          axes = [linspace(lower[i], upper[i], nbins) for i in range(lower.shape[0])]
    210
    211      H = zeros((len(axes[0]), len(axes[1])))
    212
    213      while len(x):
    214
    215          y = x[:nbatch]
    216          x = x[nbatch:]
    217
    218          I = transpose([argmin(fabs(subtract.outer(y[:, i], axes[i])), 1) for i in range(2)])
    219
    220          for i, j in I: H[i, j] += 1
    221
    222      if normalize:
    223          H = H / H.sum() / (axes[0][1] - axes[0][0]) / (axes[1][1] - axes[1][0])
    224
    225      return H, axes
    226
    227 -def histogram_nd(x, nbins=100, axes=None, nbatch=1000, normalize=True):
    228      """
    229      Non-greedy n-dimemsional histogram.
    230
    231      @param x: input array of rank (-1,n)
    232      @type x: numpy array
    233      @param nbins: number of bins
    234      @type nbins: integer
    235      @param axes: axes used for binning the data (if provided this will be used instead of <nbins>)
    236      @type axes: tuple of two one-dimensional numpy arrays
    237      @param nbatch: size of batch that is used to sort the data into the nD grid
    238      @type nbatch: integer
    239      @param normalize: specifies whether histogram should be normalized
    240      @type normalize: boolean
    241
    242      @return: n-rank array storing histogram, tuple of axes
    243      """
    244      import numpy as np
    245
    246      if len(x.shape) == 1:
    247          x = np.reshape(x, (-1,1))
    248
    249      d = x.shape[1]
    250
    251      if axes is None:
    252
    253          lower, upper = x.min(0), x.max(0)
    254          axes = [np.linspace(lower[i], upper[i], nbins) for i in range(d)]
    255
    256      shape = tuple(map(len, axes))
    257
    258      H = np.zeros(shape)
    259      ## MH: was like that before...
    260      ## s = np.multiply.accumulate(np.array((1,) + H.shape[:-1]))[::-1]
    261      s = np.multiply.accumulate(np.array((1,) + H.shape[1:]))[::-1]
    262      H = H.flatten()
    263
    264      while len(x):
    265
    266          y = x[:nbatch]
    267          x = x[nbatch:]
    268
    269          I = np.transpose([np.argmin(np.fabs(np.subtract.outer(y[:, i], axes[i])), 1)
    270                            for i in range(d)])
    271          I = np.dot(I, s)
    272          I = np.sort(I)
    273
    274          i = list(set(I.tolist()))
    275          n = np.equal.outer(I, i).sum(0)
    276
    277          H[i] += n
    278
    279      if normalize:
    280          H = H / H.sum() / np.multiply.reduce([axes[i][1] - axes[i][0] for i in range(d)])
    281
    282      H = np.reshape(H, shape)
    283
    284      return H, axes
    285
    286 -def density(x, nbins, normalize=True):
    287      """
    288      Histogram of univariate input data: basically calls numpy's histogram method and
    289      does a proper normalization.
    290
    291      @param x: input numpy array
    292      @param nbins: number of bins
    293      @type nbins: integer
    294      @param normalize: if true, histogram will be normalized
    295      """
    296      from numpy import histogram
    297
    298      hy, hx = histogram(x, nbins)
    299      hx = 0.5 * (hx[1:] + hx[:-1])
    300      hy = hy.astype('d')
    301      if normalize:
    302          hy /= (hx[1] - hx[0]) * hy.sum()
    303
    304      return hx, hy
    305
    306 -def circvar(a, axis=None):
    307      """
    308      Calculate circular variance of a circular variable.
    309
    310      @param a: input array
    311      @param axis: axis along which mean is calculated
    312      @type axis: None or integer
    313      """
    314      from numpy import average, cos, sin
    315      return 1 - average(cos(a), axis) ** 2 - average(sin(a), axis) ** 2
    316
    317 -def circmean(a, axis=None):
    318      """
    319      Estimate mean of a circular variable
    320
    321      @param a: input array
    322      @param axis: axis along which mean is calculated
    323      @type axis: None or integer
    324      """
    325      from numpy import sin, cos, arctan2, average
    326      return arctan2(average(sin(a), axis), average(cos(a), axis))
    327
    328 -def running_average(x, w, axis=None):
    329      """
    330      Calculates a running average for given window size
    331
    332      @param x: input array
    333      @param w: window size
    334      @type w: integer
    335      @param axis: axis along which mean is calculated
    336      """
    337      from numpy import array, mean
    338      return array([mean(x[i:i + w], axis) for i in range(len(x) - w)])
    339
    340 -def weighted_median(x, w):
    341      """
    342      Calculates the weighted median, that is the minimizer of
    343      argmin {\sum w_i |x_i - \mu|}
    344
    345      @param x: input array
    346      @param w: array of weights
    347      """
    348      from numpy import sum, add, argsort, sort
    349
    350      w = w / w.sum()
    351      w = w[argsort(x)]
    352      x = sort(x)
    353      j = sum(add.accumulate(w) < 0.5)
    354
    355      return x[j]
    356