renamed to be more readable

experiements/test_stats.py

@@ -0,0 +1,88 @@
+# -*- coding: utf-8 -*-
+"""
+
+py.test module to test stats.py module.
+
+
+Created on Wed Aug 27 06:45:06 2014
+
+@author: rlabbe
+"""
+from __future__ import division
+from math import pi, exp
+import numpy as np
+from stats import gaussian, multivariate_gaussian, _to_cov
+from numpy.linalg import inv
+from numpy import linalg
+
+
+def near_equal(x,y):
+    return abs(x-y) < 1.e-15
+
+
+def test_gaussian():
+    import scipy.stats
+
+    mean = 3.
+    var = 1.5
+    std = var**0.5
+
+    for i in np.arange(-5,5,0.1):
+        p0 = scipy.stats.norm(mean, std).pdf(i)
+        p1 = gaussian(i, mean, var)
+
+        assert near_equal(p0, p1)
+
+
+
+def norm_pdf_multivariate(x, mu, sigma):
+    """ extremely literal transcription of the multivariate equation.
+    Slow, but easy to verify by eye compared to my version."""
+
+    n = len(x)
+    sigma = _to_cov(sigma,n)
+
+    det = linalg.det(sigma)
+
+    norm_const = 1.0 / (pow((2*pi), n/2) * pow(det, .5))
+    x_mu = x - mu
+    result = exp(-0.5 * (x_mu.dot(inv(sigma)).dot(x_mu.T)))
+    return norm_const * result
+
+
+
+def test_multivariate():
+    from scipy.stats import multivariate_normal as mvn
+    from numpy.random import rand
+
+    mean = 3
+    var = 1.5
+
+    assert near_equal(mvn(mean,var).pdf(0.5),
+                      multivariate_gaussian(0.5, mean, var))
+
+    mean = np.array([2.,17.])
+    var = np.array([[10., 1.2], [1.2, 4.]])
+
+    x = np.array([1,16])
+    assert near_equal(mvn(mean,var).pdf(x),
+                      multivariate_gaussian(x, mean, var))
+
+    for i in range(100):
+        x = np.array([rand(), rand()])
+        assert near_equal(mvn(mean,var).pdf(x),
+                          multivariate_gaussian(x, mean, var))
+
+        assert near_equal(mvn(mean,var).pdf(x),
+                          norm_pdf_multivariate(x, mean, var))
+
+
+    mean = np.array([1,2,3,4])
+    var = np.eye(4)*rand()
+
+    x = np.array([2,3,4,5])
+
+    assert near_equal(mvn(mean,var).pdf(x),
+                      norm_pdf_multivariate(x, mean, var))
+
+