def test_goodness_of_fit(self):
trial = 100
vals = [self.rs.choice(3, 1, True, [0.3, 0.3, 0.4]).get()
for _ in six.moves.xrange(trial)]
counts = numpy.histogram(vals, bins=numpy.arange(4))[0]
expected = numpy.array([30, 30, 40])
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_goodness_of_fit(self):
trial = 100
vals = self.generate_many(3, 1, True, [0.3, 0.3, 0.4], _count=trial)
vals = [val.get() for val in vals]
counts = numpy.histogram(vals, bins=numpy.arange(4))[0]
expected = numpy.array([30, 30, 40])
assert hypothesis.chi_square_test(counts, expected)
def test_goodness_of_fit(self):
mx = 5
trial = 100
vals = [random.randint(mx).get() for _ in six.moves.xrange(trial)]
counts = numpy.histogram(vals, bins=numpy.arange(mx + 1))[0]
expected = numpy.array([float(trial) / mx] * mx)
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_goodness_of_fit(self):
mx = 5
trial = 100
vals = [random.randint(mx).get() for _ in six.moves.xrange(trial)]
counts = numpy.histogram(vals, bins=numpy.arange(mx + 1))[0]
expected = numpy.array([float(trial) / mx] * mx)
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_goodness_of_fit(self):
trial = 100
vals = [self.rs.choice(3, 1, True, [0.3, 0.3, 0.4]).get()
for _ in six.moves.xrange(trial)]
counts = numpy.histogram(vals, bins=numpy.arange(4))[0]
expected = numpy.array([30, 30, 40])
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_goodness_of_fit(self):
mx = 5
trial = 300
vals = self.generate_many(mx, None, _count=trial)
vals = [val.get() for val in vals]
counts = numpy.histogram(vals, bins=numpy.arange(mx + 2))[0]
expected = numpy.array([float(trial) / (mx + 1)] * (mx + 1))
assert hypothesis.chi_square_test(counts, expected)
def test_goodness_of_fit(self):
mx = 5
trial = 100
vals = [self.rs.interval(mx, None).get()
for _ in six.moves.xrange(trial)]
counts = numpy.histogram(vals, bins=numpy.arange(mx + 2))[0]
expected = numpy.array([float(trial) / (mx + 1)] * (mx + 1))
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_goodness_of_fit_2(self):
vals = self.generate(3, (5, 20), True, [0.3, 0.3, 0.4]).get()
counts = numpy.histogram(vals, bins=numpy.arange(4))[0]
expected = numpy.array([30, 30, 40])
assert hypothesis.chi_square_test(counts, expected)
def test_goodness_of_fit_2(self):
mx = 5
vals = self.generate(mx, (5, 5)).get()
counts = numpy.histogram(vals, bins=numpy.arange(mx + 2))[0]
expected = numpy.array([float(vals.size) / (mx + 1)] * (mx + 1))
assert hypothesis.chi_square_test(counts, expected)
def test_goodness_of_fit_2(self):
mx = 5
vals = self.rs.interval(mx, (5, 5)).get()
counts = numpy.histogram(vals, bins=numpy.arange(mx + 2))[0]
expected = numpy.array([float(vals.size) / mx + 1] * (mx + 1))
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_goodness_of_fit_2(self):
mx = 5
vals = random.randint(mx, size=(5, 20)).get()
counts = numpy.histogram(vals, bins=numpy.arange(mx + 1))[0]
expected = numpy.array([float(vals.size) / mx] * mx)
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_goodness_of_fit_2(self):
mx = 5
vals = self.rs.interval(mx, (5, 5)).get()
counts = numpy.histogram(vals, bins=numpy.arange(mx + 2))[0]
expected = numpy.array([float(vals.size) / mx + 1] * (mx + 1))
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_goodness_of_fit_2(self):
mx = 5
vals = random.randint(mx, size=(5, 20)).get()
counts = numpy.histogram(vals, bins=numpy.arange(mx + 1))[0]
expected = numpy.array([float(vals.size) / mx] * mx)
self.assertTrue(hypothesis.chi_square_test(counts, expected))
def test_goodness_of_fit_2(self):
vals = self.rs.choice(3, (5, 20), True, [0.3, 0.3, 0.4]).get()
counts = numpy.histogram(vals, bins=numpy.arange(4))[0]
expected = numpy.array([30, 30, 40])
self.assertTrue(hypothesis.chi_square_test(counts, expected))