/usr/share/pyshared/mvpa/misc/stats.py is in python-mvpa 0.4.8-3.
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
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"""Little statistics helper"""
__docformat__ = 'restructuredtext'
from mvpa.base import externals
if externals.exists('scipy', raiseException=True):
import scipy.stats as stats
import numpy as N
import copy
def chisquare(obs, exp=None):
"""Compute the chisquare value of a contingency table with arbitrary
dimensions.
If no expected frequencies are supplied, the total N is assumed to be
equally distributed across all cells.
Returns: chisquare-stats, associated p-value (upper tail)
"""
obs = N.array(obs)
# get total number of observations
nobs = N.sum(obs)
# if no expected value are supplied assume equal distribution
if exp == None:
exp = N.ones(obs.shape) * nobs / N.prod(obs.shape)
# make sure to have floating point data
exp = exp.astype(float)
# compute chisquare value
chisq = N.sum((obs - exp )**2 / exp)
# return chisq and probability (upper tail)
return chisq, stats.chisqprob(chisq, N.prod(obs.shape) - 1)
class DSMatrix(object):
"""DSMatrix allows for the creation of dissilimarity matrices using
arbitrary distance metrics.
"""
# metric is a string
def __init__(self, data_vectors, metric='spearman'):
"""Initialize DSMatrix
:Parameters:
data_vectors : ndarray
m x n collection of vectors, where m is the number of exemplars
and n is the number of features per exemplar
metric : string
Distance metric to use (e.g., 'euclidean', 'spearman', 'pearson',
'confusion')
"""
# init members
self.full_matrix = []
self.u_triangle = None
self.vector_form = None
# this one we know straight away, so set it
self.metric = metric
# size of dataset (checking if we're dealing with a column vector only)
num_exem = N.shape(data_vectors)[0]
flag_1d = False
# changed 4/26/09 to new way of figuring out if array is 1-D
#if (isinstance(data_vectors, N.ndarray)):
if (not(num_exem == N.size(data_vectors))):
num_features = N.shape(data_vectors)[1]
else:
flag_1d = True
num_features = 1
# generate output (dissimilarity) matrix
dsmatrix = N.mat(N.zeros((num_exem, num_exem)))
if (metric == 'euclidean'):
#print 'Using Euclidean distance metric...'
# down rows
for i in range(num_exem):
# across columns
for j in range(num_exem):
if (not(flag_1d)):
dsmatrix[i, j] = N.linalg.norm(
data_vectors[i, :] - data_vectors[j, :])
else:
dsmatrix[i, j] = N.linalg.norm(
data_vectors[i] - data_vectors[j])
elif (metric == 'spearman'):
#print 'Using Spearman rank-correlation metric...'
# down rows
for i in range(num_exem):
# across columns
for j in range(num_exem):
dsmatrix[i, j] = 1 - stats.spearmanr(
data_vectors[i,:], data_vectors[j,:])[0]
elif (metric == 'pearson'):
#print 'Using Pearson correlation metric...'
# down rows
for i in range(num_exem):
# across columns
for j in range(num_exem):
dsmatrix[i, j] = 1 - stats.pearsonr(
data_vectors[i,:], data_vectors[j,:])[0]
elif (metric == 'confusion'):
#print 'Using confusion correlation metric...'
# down rows
for i in range(num_exem):
# across columns
for j in range(num_exem):
if (not(flag_1d)):
dsmatrix[i, j] = 1 - int(
N.floor(N.sum((
data_vectors[i, :] == data_vectors[j, :]
).astype(N.int32)) / num_features))
else:
dsmatrix[i, j] = 1 - int(
data_vectors[i] == data_vectors[j])
self.full_matrix = dsmatrix
def getTriangle(self):
# if we need to create the u_triangle representation, do so
if (self.u_triangle is None):
self.u_triangle = N.triu(self.full_matrix)
return self.u_triangle
# create the dissimilarity matrix on the (upper) triangle of the two
# two dissimilarity matrices; we can just reuse the same dissimilarity
# matrix code, but since it will return a matrix, we need to pick out
# either dsm[0,1] or dsm[1,0]
# note: this is a bit of a kludge right now, but it's the only way to solve
# certain problems:
# 1. Set all 0-valued elements in the original matrix to -1 (an impossible
# value for a dissimilarity matrix)
# 2. Find the upper triangle of the matrix
# 3. Create a vector from the upper triangle, but only with the
# elements whose absolute value is greater than 0 -- this
# will keep everything from the original matrix that wasn't
# part of the zero'ed-out portion when we took the upper
# triangle
# 4. Set all the -1-valued elements in the vector to 0 (their
# original value)
# 5. Cast to numpy array
def getVectorForm(self):
if (self.vector_form is not None):
return self.vector_form
orig_dsmatrix = copy.deepcopy(self.getFullMatrix())
orig_dsmatrix[orig_dsmatrix == 0] = -1
orig_tri = N.triu(orig_dsmatrix)
vector_form = orig_tri[abs(orig_tri) > 0]
vector_form[vector_form == -1] = 0
vector_form = N.asarray(vector_form)
self.vector_form = vector_form[0]
return self.vector_form
# XXX is there any reason to have these get* methods
# instead of plain access to full_matrix and method?
def getFullMatrix(self):
return self.full_matrix
def getMetric(self):
return self.metric
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