def test_skipping_classes(self):
X, y = self.samp.custom_distribution(0, 128, [64,64,0,0,0])
collect = {}
for k in set(y):
collect[k] = []
for i, klass in enumerate(y):
collect[klass].append(X[i])
self.failUnless(
sum(abs(get_proportions(collect) - numpy.array([0.5, 0.5])) < PROPORTION_ERROR_MARGIN) == 2
)
def test_custom_distribution(self):
X, y = self.samp.custom_distribution(0, 128, [94,9,19,3,3])
collect = {}
for k in set(y):
collect[k] = []
for i, klass in enumerate(y):
collect[klass].append(X[i])
self.failUnless(
sum(abs(get_proportions(collect) - self.true_proportions) < PROPORTION_ERROR_MARGIN) == self.K
)
def test_skipping_classes(self):
X, y = self.samp.custom_distribution(0, 128, [64, 64, 0, 0, 0])
collect = {}
for k in set(y):
collect[k] = []
for i, klass in enumerate(y):
collect[klass].append(X[i])
self.failUnless(
sum(
abs(get_proportions(collect) -
numpy.array([0.5, 0.5])) < PROPORTION_ERROR_MARGIN) == 2)
def test_custom_distribution(self):
X, y = self.samp.custom_distribution(0, 128, [94, 9, 19, 3, 3])
collect = {}
for k in set(y):
collect[k] = []
for i, klass in enumerate(y):
collect[klass].append(X[i])
self.failUnless(
sum(
abs(get_proportions(collect) - self.true_proportions) <
PROPORTION_ERROR_MARGIN) == self.K)
