diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 85 |
1 files changed, 56 insertions, 29 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 03a4ef0b6..7c61180f3 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -1,27 +1,24 @@ __docformat__ = "restructuredtext en" -__all__ = ['select', 'piecewise', 'trim_zeros', - 'copy', 'iterable', 'percentile', - 'diff', 'gradient', 'angle', 'unwrap', 'sort_complex', 'disp', - 'extract', 'place', 'nansum', 'nanmax', 'nanargmax', - 'nanargmin', 'nanmin', 'vectorize', 'asarray_chkfinite', 'average', - 'histogram', 'histogramdd', 'bincount', 'digitize', 'cov', - 'corrcoef', 'msort', 'median', 'sinc', 'hamming', 'hanning', - 'bartlett', 'blackman', 'kaiser', 'trapz', 'i0', 'add_newdoc', - 'add_docstring', 'meshgrid', 'delete', 'insert', 'append', - 'interp' - ] -import warnings +__all__ = ['select', 'piecewise', 'trim_zeros', 'copy', 'iterable', + 'percentile', 'diff', 'gradient', 'angle', 'unwrap', 'sort_complex', + 'disp', 'extract', 'place', 'nansum', 'nanmax', 'nanargmax', + 'nanargmin', 'nanmin', 'vectorize', 'asarray_chkfinite', 'average', + 'histogram', 'histogramdd', 'bincount', 'digitize', 'cov', 'corrcoef', + 'msort', 'median', 'sinc', 'hamming', 'hanning', 'bartlett', + 'blackman', 'kaiser', 'trapz', 'i0', 'add_newdoc', 'add_docstring', + 'meshgrid', 'delete', 'insert', 'append', 'interp'] +import warnings import types import sys import numpy.core.numeric as _nx from numpy.core import linspace from numpy.core.numeric import ones, zeros, arange, concatenate, array, \ - asarray, asanyarray, empty, empty_like, ndarray, around + asarray, asanyarray, empty, empty_like, ndarray, around from numpy.core.numeric import ScalarType, dot, where, newaxis, intp, \ - integer, isscalar + integer, isscalar from numpy.core.umath import pi, multiply, add, arctan2, \ - frompyfunc, isnan, cos, less_equal, sqrt, sin, mod, exp, log10 + frompyfunc, isnan, cos, less_equal, sqrt, sin, mod, exp, log10 from numpy.core.fromnumeric import ravel, nonzero, choose, sort, mean from numpy.core.numerictypes import typecodes, number from numpy.core import atleast_1d, atleast_2d @@ -1817,7 +1814,7 @@ class vectorize(object): for x, c in zip(self.ufunc(*newargs), self.otypes)]) return _res -def cov(m, y=None, rowvar=1, bias=0): +def cov(m, y=None, rowvar=1, bias=0, ddof=None): """ Estimate a covariance matrix, given data. @@ -1844,7 +1841,13 @@ def cov(m, y=None, rowvar=1, bias=0): bias : int, optional Default normalization is by ``(N-1)``, where ``N`` is the number of observations given (unbiased estimate). If `bias` is 1, then - normalization is by ``N``. + normalization is by ``N``. Deprecated in numpy 1.5, use ddof + instead. + ddof : int, optional + Normalization is by ``(N - ddof)``, where ``N`` is the number of + observations. Setting ddof=1 gives the usual unbiased estimate. + Default will be ``None`` until the `bias` keyword is removed and + will be 0 thereafter. Returns ------- @@ -1868,7 +1871,7 @@ def cov(m, y=None, rowvar=1, bias=0): Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance matrix shows this clearly: - >>> np.cov(x) + >>> np.cov(x, ddof=1) array([[ 1., -1.], [-1., 1.]]) @@ -1880,13 +1883,13 @@ def cov(m, y=None, rowvar=1, bias=0): >>> x = [-2.1, -1, 4.3] >>> y = [3, 1.1, 0.12] >>> X = np.vstack((x,y)) - >>> print np.cov(X) + >>> print np.cov(X, ddof=1) [[ 11.71 -4.286 ] [ -4.286 2.14413333]] - >>> print np.cov(x, y) + >>> print np.cov(x, y, ddof=1) [[ 11.71 -4.286 ] [ -4.286 2.14413333]] - >>> print np.cov(x) + >>> print np.cov(x, ddof=1) 11.71 """ @@ -1904,7 +1907,7 @@ def cov(m, y=None, rowvar=1, bias=0): if y is not None: y = array(y, copy=False, ndmin=2, dtype=float) - X = concatenate((X,y),axis) + X = concatenate((X,y), axis) X -= X.mean(axis=1-axis)[tup] if rowvar: @@ -1912,17 +1915,25 @@ def cov(m, y=None, rowvar=1, bias=0): else: N = X.shape[0] - if bias: - fact = N*1.0 - else: - fact = N-1.0 + if ddof is None: + if bias: + msg = "The bias keyword is deprecated, "\ + "use ddof=0 instead of bias=1" + ddof = 0 + else: + msg = "The bias keyword is deprecated, "\ + "use ddof=1 instead of bias=0" + ddof = 1 + warnings.warn(msg, DeprecationWarning) + fact = N - ddof if not rowvar: return (dot(X.T, X.conj()) / fact).squeeze() else: return (dot(X, X.T.conj()) / fact).squeeze() -def corrcoef(x, y=None, rowvar=1, bias=0): + +def corrcoef(x, y=None, rowvar=1, bias=0, ddof=None): """ Return correlation coefficients. @@ -1951,7 +1962,13 @@ def corrcoef(x, y=None, rowvar=1, bias=0): bias : int, optional Default normalization is by ``(N-1)``, where ``N`` is the number of observations given (unbiased estimate). If `bias` is 1, then - normalization is by ``N``. + normalization is by ``N``. Deprecated in numpy 1.5, use ddof + instead. + ddof : int, optional + Normalization is by ``(N - ddof)``, where ``N`` is the number of + observations. Setting ddof=1 gives the usual unbiased estimate. + Default will be ``None`` until the `bias` keyword is removed and + will be 0 thereafter. Returns ------- @@ -1963,7 +1980,17 @@ def corrcoef(x, y=None, rowvar=1, bias=0): cov : Covariance matrix """ - c = cov(x, y, rowvar, bias) + if ddof is None: + if bias: + msg = "The bias keyword is deprecated, "\ + "use ddof=0 instead of bias=1" + ddof = 0 + else: + msg = "The bias keyword is deprecated, "\ + "use ddof=1 instead of bias=0" + ddof = 1 + warnings.warn(msg, DeprecationWarning) + c = cov(x, y, ddof=ddof) try: d = diag(c) except ValueError: # scalar covariance |