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test_normal_full.py
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test_normal_full.py
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# test_normal_full.py
# Contact: Jacob Schreiber <jmschreiber91@gmail.com>
import os
import numpy
import torch
import pytest
from pomegranate.distributions import Normal
from ._utils import _test_initialization_raises_two_parameters
from ._utils import _test_initialization
from ._utils import _test_predictions
from ._utils import _test_efd_from_summaries
from ._utils import _test_raises
from nose.tools import assert_raises
from numpy.testing import assert_array_almost_equal
SQRT_2_PI = 2.50662827463
LOG_2_PI = 1.83787706641
MIN_VALUE = None
MAX_VALUE = None
VALID_VALUE = 1.2
@pytest.fixture
def X():
return [[1.1, 2.1, 0.5],
[0.5, 0.2, 1.3],
[1.4, 1.1, 2.2],
[3.1, 2.1, 2.2],
[3.4, 1.0, 0.3],
[5.4, 1.9, 4.0],
[2.2, 1.3, 0.1]]
@pytest.fixture
def w():
return [[1], [2], [0], [0], [5], [1], [2]]
@pytest.fixture
def means():
return [1.2, 1.8, 2.1]
@pytest.fixture
def covs():
return [
[0.3, 0.1, 0.0],
[0.1, 2.1, 0.6],
[0.0, 0.6, 1.2]
]
###
def test_initialization():
d = Normal(covariance_type='full')
_test_initialization(d, None, "means", 0.0, False, None)
_test_initialization(d, None, "covs", 0.0, False, None)
assert_raises(AttributeError, getattr, d, "_w_sum")
assert_raises(AttributeError, getattr, d, "_xw_sum")
assert_raises(AttributeError, getattr, d, "_xxw_sum")
assert_raises(AttributeError, getattr, d, "_inv_cov")
assert_raises(AttributeError, getattr, d, "_inv_cov_dot_mu")
assert_raises(AttributeError, getattr, d, "_log_det")
assert_raises(AttributeError, getattr, d, "_log_sigma_sqrt_2pi")
def test_initialization_float(means, covs):
funcs = (lambda x: x, tuple,
lambda x: numpy.array(x, dtype=numpy.float32),
lambda x: torch.tensor(x, dtype=torch.float32),
lambda x: torch.nn.Parameter(torch.tensor(x), requires_grad=False))
for func in funcs:
means_, covs_ = func(means), func(covs)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=0.0, frozen=False), means_, "means", 0.0, False,
torch.float32)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=0.3, frozen=False), means_, "means", 0.3, False,
torch.float32)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=1.0, frozen=True), means_, "means", 1.0, True,
torch.float32)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=1.0, frozen=False), means_, "means", 1.0, False,
torch.float32)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=0.0, frozen=False), covs_, "covs", 0.0, False,
torch.float32)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=0.3, frozen=False), covs_, "covs", 0.3, False,
torch.float32)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=1.0, frozen=True), covs_, "covs", 1.0, True,
torch.float32)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=1.0, frozen=False), covs_, "covs", 1.0, False,
torch.float32)
means_ = numpy.array(means, dtype=numpy.float64)
covs_ = numpy.array(covs, dtype=numpy.float64)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=0.0, frozen=False), means_, "means", 0.0, False, torch.float64)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=0.3, frozen=False), means_, "means", 0.3, False, torch.float64)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=1.0, frozen=True), means_, "means", 1.0, True, torch.float64)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=1.0, frozen=False), means_, "means", 1.0, False, torch.float64)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=0.0, frozen=False), covs_, "covs", 0.0, False, torch.float64)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=0.3, frozen=False), covs_, "covs", 0.3, False, torch.float64)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=1.0, frozen=True), covs_, "covs", 1.0, True, torch.float64)
_test_initialization(Normal(means_, covs_, covariance_type='full',
inertia=1.0, frozen=False), covs_, "covs", 1.0, False, torch.float64)
def test_initialization_raises():
_test_initialization_raises_two_parameters(Normal, VALID_VALUE, VALID_VALUE,
min_value1=MIN_VALUE, min_value2=MIN_VALUE)
def test_reset_cache(X):
d = Normal(covariance_type='full')
d.summarize(X)
assert_array_almost_equal(d._w_sum, [7.0, 7.0, 7.0])
assert_array_almost_equal(d._xw_sum, [17.099998, 9.7, 10.599999], 4)
assert_array_almost_equal(d._xxw_sum,
[[58.59 , 26.98 , 33.940002],
[26.98 , 16.369999, 16.38 ],
[33.940002, 16.38 , 27.720001]], 4)
d._reset_cache()
assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xw_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xxw_sum,
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
d = Normal()
assert_raises(AttributeError, getattr, d, "_w_sum")
assert_raises(AttributeError, getattr, d, "_xw_sum")
assert_raises(AttributeError, getattr, d, "_xxw_sum")
assert_raises(AttributeError, getattr, d, "_inv_cov")
assert_raises(AttributeError, getattr, d, "_inv_cov_dot_mu")
assert_raises(AttributeError, getattr, d, "_log_det")
assert_raises(AttributeError, getattr, d, "_log_sigma_sqrt_2pi")
d._reset_cache()
assert_raises(AttributeError, getattr, d, "_w_sum")
assert_raises(AttributeError, getattr, d, "_xw_sum")
assert_raises(AttributeError, getattr, d, "_xxw_sum")
assert_raises(AttributeError, getattr, d, "_inv_cov")
assert_raises(AttributeError, getattr, d, "_inv_cov_dot_mu")
assert_raises(AttributeError, getattr, d, "_log_det")
assert_raises(AttributeError, getattr, d, "_log_sigma_sqrt_2pi")
def test_initialize(X):
d = Normal(covariance_type='full')
assert d.d is None
assert d.means is None
assert d.covs is None
assert d._initialized == False
assert_raises(AttributeError, getattr, d, "_w_sum")
assert_raises(AttributeError, getattr, d, "_xw_sum")
assert_raises(AttributeError, getattr, d, "_xxw_sum")
assert_raises(AttributeError, getattr, d, "_inv_cov")
assert_raises(AttributeError, getattr, d, "_inv_cov_dot_mu")
assert_raises(AttributeError, getattr, d, "_log_det")
assert_raises(AttributeError, getattr, d, "_log_sigma_sqrt_2pi")
d = Normal([1.2], None, covariance_type='full')
assert d.d is None
assert d.covs is None
assert d._initialized == False
assert_raises(AttributeError, getattr, d, "_w_sum")
assert_raises(AttributeError, getattr, d, "_xw_sum")
assert_raises(AttributeError, getattr, d, "_xxw_sum")
assert_raises(AttributeError, getattr, d, "_inv_cov")
assert_raises(AttributeError, getattr, d, "_inv_cov_dot_mu")
assert_raises(AttributeError, getattr, d, "_log_det")
assert_raises(AttributeError, getattr, d, "_log_sigma_sqrt_2pi")
d = Normal(None, [[1.2]], covariance_type='full')
assert d.d is None
assert d.means is None
assert d._initialized == False
assert_raises(AttributeError, getattr, d, "_w_sum")
assert_raises(AttributeError, getattr, d, "_xw_sum")
assert_raises(AttributeError, getattr, d, "_xxw_sum")
assert_raises(AttributeError, getattr, d, "_inv_cov")
assert_raises(AttributeError, getattr, d, "_inv_cov_dot_mu")
assert_raises(AttributeError, getattr, d, "_log_det")
assert_raises(AttributeError, getattr, d, "_log_sigma_sqrt_2pi")
d._initialize(3)
assert d._initialized == True
assert d.means.shape[0] == 3
assert d.d == 3
assert_array_almost_equal(d.means, [0.0, 0.0, 0.0])
assert_array_almost_equal(d.covs,
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xw_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xxw_sum,
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
d._initialize(2)
assert d._initialized == True
assert d.means.shape[0] == 2
assert d.d == 2
assert_array_almost_equal(d.means, [0.0, 0.0])
assert_array_almost_equal(d.covs,
[[0., 0.],
[0., 0.]])
assert_array_almost_equal(d._w_sum, [0.0, 0.0])
assert_array_almost_equal(d._xw_sum, [0.0, 0.0])
assert_array_almost_equal(d._xxw_sum,
[[0., 0.],
[0., 0.]])
d = Normal([1.2, 9.3], [[1.1, 1.2], [1.2, 1.5]], covariance_type='full')
assert d._initialized == True
assert d.d == 2
d._initialize(3)
assert d._initialized == True
assert d.means.shape[0] == 3
assert d.d == 3
assert_array_almost_equal(d.means, [0.0, 0.0, 0.0])
assert_array_almost_equal(d.covs,
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
d = Normal(covariance_type='full')
d.summarize(X)
d._initialize(4)
assert d._initialized == True
assert d.means.shape[0] == 4
assert d.d == 4
assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0, 0.0])
assert_array_almost_equal(d._xw_sum, [0.0, 0.0, 0.0, 0.0])
assert_array_almost_equal(d._xxw_sum,
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]])
###
@pytest.mark.sample
def test_sample(means, covs):
torch.manual_seed(0)
X = Normal(means, covs, covariance_type='full').sample(1)
assert_array_almost_equal(X, [[2.044038, 1.659515, -0.229191]])
X = Normal(means, covs, covariance_type='full').sample(5)
assert_array_almost_equal(X,
[[1.5113, 0.3447, 0.2308],
[1.4209, 3.0784, 1.7213],
[0.9791, 0.8686, 2.0354],
[0.7308, 3.2258, 1.4744],
[1.2672, 1.0083, 2.2415]], 3)
###
def test_probability(X, means, covs):
m, c = [1.7], [[1.3]]
x = [[1.0], [2.0], [4.0], [3.7], [1.9]]
y = [0.289795, 0.337991, 0.045742, 0.075126, 0.344554]
d1 = Normal(m, c, covariance_type='full')
d2 = Normal(numpy.array(m, dtype=numpy.float64),
numpy.array(c, dtype=numpy.float64), covariance_type='full')
_test_predictions(x, y, d1.probability(x), torch.float32)
_test_predictions(x, y, d2.probability(x), torch.float64)
x = [[1.0, 2.0, 4.0]]
y = [0.014501]
d1 = Normal(means, covs, covariance_type='full')
d2 = Normal(numpy.array(means, dtype=numpy.float64),
numpy.array(covs, dtype=numpy.float64), covariance_type='full')
_test_predictions(x, y, d1.probability(x), torch.float32)
_test_predictions(x, y, d2.probability(x), torch.float64)
y = [1.873443e-02, 2.068522e-02, 6.141333e-02, 1.854071e-04,
5.782201e-06, 7.198527e-16, 2.817989e-03]
d1 = Normal(means, covs, covariance_type='full')
d2 = Normal(numpy.array(means, dtype=numpy.float64),
numpy.array(covs, dtype=numpy.float64), covariance_type='full')
_test_predictions(X, y, d1.probability(X), torch.float32)
_test_predictions(X, y, d2.probability(X), torch.float64)
def test_probability_dtypes(X, means, covs):
y = Normal(means, covs, covariance_type='full').probability(X)
assert y.dtype == torch.float32
def test_probability_raises(X, means, covs):
_test_raises(Normal(means, covs, covariance_type='full'), "probability", X,
min_value=MIN_VALUE, max_value=MAX_VALUE)
_test_raises(Normal([VALID_VALUE], [[VALID_VALUE]], covariance_type='full'),
"probability", X, min_value=MIN_VALUE, max_value=MAX_VALUE)
def test_log_probability(X, means, covs):
m, c = [1.7], [[1.3]]
x = [[1.0], [2.0], [4.0], [3.0], [2.0]]
y = [-1.238582, -1.084736, -3.084736, -1.700121, -1.084736]
x_torch = torch.tensor(numpy.array(x))
m_torch = torch.tensor(numpy.array(m))
c_torch = torch.tensor(numpy.array(c))
d1 = Normal(m, c, covariance_type='full')
d2 = Normal(numpy.array(m, dtype=numpy.float64),
numpy.array(c, dtype=numpy.float64), covariance_type='full')
d3 = torch.distributions.MultivariateNormal(m_torch, c_torch)
_test_predictions(x, y, d1.log_probability(x), torch.float32)
_test_predictions(x, y, d2.log_probability(x), torch.float64)
_test_predictions(x, d3.log_prob(x_torch), d2.log_probability(x),
torch.float64)
p = [1.7, 2.3, 1.0, 1.7, 4.1]
x = [[1.0, 2.0, 4.0, 3.0, 2.0]]
y = [-12.434692]
c = numpy.eye(5).astype(numpy.float32)
p_torch = torch.tensor(numpy.array(p))
x_torch = torch.tensor(numpy.array(x))
d1 = Normal(p, c, covariance_type='full')
d2 = Normal(numpy.array(p, dtype=numpy.float64),
numpy.array(c, dtype=numpy.float64), covariance_type='full')
d3 = torch.distributions.MultivariateNormal(p_torch, torch.eye(5))
_test_predictions(x, y, d1.log_probability(x), torch.float32)
_test_predictions(x, y, d2.log_probability(x), torch.float64)
_test_predictions(x, d3.log_prob(x_torch), d2.log_probability(x),
torch.float64)
y = [-3.977393, -3.878336, -2.790128, -8.592959, -12.060726,
-34.867487, -5.871732]
m_torch = torch.tensor(numpy.array(means))
c_torch = torch.tensor(numpy.array(covs))
x_torch = torch.tensor(numpy.array(X))
d2 = Normal(numpy.array(means, dtype=numpy.float64),
numpy.array(covs, dtype=numpy.float64), covariance_type='full')
d3 = torch.distributions.MultivariateNormal(m_torch, c_torch)
_test_predictions(X, y, d2.log_probability(X), torch.float64)
_test_predictions(X, d3.log_prob(x_torch), d2.log_probability(X),
torch.float64)
def test_log_probability_dtypes(X, means, covs):
y = Normal(means, covs, covariance_type='full').log_probability(X)
assert y.dtype == torch.float32
def test_log_probability_raises(X, means, covs):
_test_raises(Normal(means, covs, covariance_type='full'), "log_probability",
X, min_value=MIN_VALUE, max_value=MAX_VALUE)
_test_raises(Normal([VALID_VALUE], [[VALID_VALUE]], covariance_type='full'),
"log_probability", X, min_value=MIN_VALUE, max_value=MAX_VALUE)
###
def test_summarize(X, means, covs):
for m, c in ((means, covs), (None, None)):
d = Normal(m, c, covariance_type='full')
d.summarize(X[:4])
assert_array_almost_equal(d._w_sum, [4.0, 4.0, 4.0])
assert_array_almost_equal(d._xw_sum, [6.1, 5.5, 6.2])
assert_array_almost_equal(d._xxw_sum,
[[13.03 , 10.459999, 11.1 ],
[10.459999, 10.07 , 8.35 ],
[11.1 , 8.35 , 11.620001]], 4)
d.summarize(X[4:])
assert_array_almost_equal(d._w_sum, [7.0, 7.0, 7.0])
assert_array_almost_equal(d._xw_sum, [17.1, 9.7, 10.6])
assert_array_almost_equal(d._xxw_sum,
[[58.59 , 26.98 , 33.940002],
[26.98 , 16.369999, 16.380001],
[33.940002, 16.380001, 27.720001]], 4)
d = Normal(m, c, covariance_type='full')
d.summarize(X)
assert_array_almost_equal(d._w_sum, [7.0, 7.0, 7.0])
assert_array_almost_equal(d._xw_sum, [17.099998, 9.7 , 10.599999])
assert_array_almost_equal(d._xxw_sum,
[[58.59 , 26.98 , 33.940002],
[26.98 , 16.369999, 16.38 ],
[33.940002, 16.38 , 27.720001]], 4)
def test_summarize_weighted(X, w, means, covs):
d = Normal(means, covs, covariance_type='full')
d.summarize(X[:4], sample_weight=w[:4])
assert_array_almost_equal(d._w_sum, [3., 3., 3.])
assert_array_almost_equal(d._xw_sum, [2.1, 2.5, 3.1])
assert_array_almost_equal(d._xxw_sum,
[[1.71 , 2.51 , 1.85 ],
[2.51 , 4.489999, 1.57 ],
[1.85 , 1.57 , 3.63 ]], 4)
d.summarize(X[4:], sample_weight=w[4:])
assert_array_almost_equal(d._w_sum, [11.0, 11.0, 11.0])
assert_array_almost_equal(d._xw_sum, [28.9 , 12. , 8.799999])
assert_array_almost_equal(d._xxw_sum,
[[98.350006, 35.489998, 28.990002],
[35.489998, 16.48 , 10.93 ],
[28.990002, 10.93 , 20.1 ]], 5)
d = Normal(means, covs, covariance_type='full')
d.summarize(X, sample_weight=w)
assert_array_almost_equal(d._w_sum, [11.0, 11.0, 11.0])
assert_array_almost_equal(d._xw_sum, [28.9 , 12. , 8.799999])
assert_array_almost_equal(d._xxw_sum,
[[98.350006, 35.49 , 28.99 ],
[35.49 , 16.48 , 10.93 ],
[28.99 , 10.93 , 20.1 ]], 5)
def test_summarize_weighted_flat(X, w, means, covs):
w = numpy.array(w)[:,0]
d = Normal(means, covs, covariance_type='full')
d.summarize(X[:4], sample_weight=w[:4])
assert_array_almost_equal(d._w_sum, [3., 3., 3.])
assert_array_almost_equal(d._xw_sum, [2.1, 2.5, 3.1])
assert_array_almost_equal(d._xxw_sum,
[[1.71 , 2.51 , 1.85 ],
[2.51 , 4.489999, 1.57 ],
[1.85 , 1.57 , 3.63 ]], 4)
d.summarize(X[4:], sample_weight=w[4:])
assert_array_almost_equal(d._w_sum, [11.0, 11.0, 11.0])
assert_array_almost_equal(d._xw_sum, [28.9 , 12. , 8.799999])
assert_array_almost_equal(d._xxw_sum,
[[98.350006, 35.489998, 28.990002],
[35.489998, 16.48 , 10.93 ],
[28.990002, 10.93 , 20.1 ]], 5)
d = Normal(means, covs, covariance_type='full')
d.summarize(X, sample_weight=w)
assert_array_almost_equal(d._w_sum, [11.0, 11.0, 11.0])
assert_array_almost_equal(d._xw_sum, [28.9 , 12. , 8.799999])
assert_array_almost_equal(d._xxw_sum,
[[98.350006, 35.49 , 28.99 ],
[35.49 , 16.48 , 10.93 ],
[28.99 , 10.93 , 20.1 ]], 5)
def test_summarize_weighted_2d(X):
d = Normal(covariance_type='full')
d.summarize(X[:4], sample_weight=X[:4])
assert_array_almost_equal(d._w_sum, [6.1, 5.5, 6.2])
assert_array_almost_equal(d._xw_sum, [13.03 , 10.069999, 11.62])
assert_array_almost_equal(d._xxw_sum,
[[33.990997, 24.928 , 26.383999],
[20.235998, 19.860996, 14.620998],
[22.9 , 16.351 , 23.618 ]], 5)
d.summarize(X[4:], sample_weight=X[4:])
assert_array_almost_equal(d._w_sum, [17.1, 9.7, 10.6])
assert_array_almost_equal(d._xw_sum, [58.59 , 16.369999, 27.720001])
assert_array_almost_equal(d._xxw_sum,
[[241.40701, 98.18401, 146.97601],
[ 46.848 , 29.917 , 29.53 ],
[109.62801, 46.854 , 87.646 ]], 5)
d = Normal(covariance_type='full')
d.summarize(X, sample_weight=X)
assert_array_almost_equal(d._w_sum, [17.099998, 9.7 , 10.599999])
assert_array_almost_equal(d._xw_sum, [58.59 , 16.369999, 27.720001])
assert_array_almost_equal(d._xxw_sum,
[[241.40701, 98.18401, 146.97601],
[ 46.848 , 29.917 , 29.53 ],
[109.62801, 46.854 , 87.646 ]], 5)
def test_summarize_dtypes(X, w, means, covs):
X = numpy.array(X)
X = X.astype(numpy.float32)
d = Normal(means, covs, covariance_type='full')
assert d._xw_sum.dtype == torch.float32
d.summarize(X)
assert d._xw_sum.dtype == torch.float32
X = X.astype(numpy.float64)
d = Normal(means, covs, covariance_type='full').type(torch.float64)
assert d._xw_sum.dtype == torch.float64
d.summarize(X)
assert d._xw_sum.dtype == torch.float64
X = X.astype(numpy.int32)
d = Normal(means, covs, covariance_type='full')
assert d._xw_sum.dtype == torch.float32
d.summarize(X)
assert d._xw_sum.dtype == torch.float32
X = X.astype(numpy.int64)
d = Normal(means, covs, covariance_type='full')
assert d._xw_sum.dtype == torch.float32
d.summarize(X)
assert d._xw_sum.dtype == torch.float32
def test_summarize_raises(X, w, means, covs):
_test_raises(Normal(means, covs, covariance_type='full'), "summarize", X,
w=w, min_value=MIN_VALUE, max_value=MAX_VALUE)
_test_raises(Normal(covariance_type='full'), "summarize", X, w=w,
min_value=MIN_VALUE, max_value=MAX_VALUE)
_test_raises(Normal([VALID_VALUE], [[VALID_VALUE]], covariance_type='full'),
"summarize", X, w=w, min_value=MIN_VALUE, max_value=MAX_VALUE)
def _test_fit_params(d, means, covs):
assert_array_almost_equal(d.means, means)
assert_array_almost_equal(d.covs, covs)
assert_array_almost_equal(d._w_sum, numpy.zeros(d.d))
assert_array_almost_equal(d._xw_sum, numpy.zeros(d.d))
assert_array_almost_equal(d._xxw_sum, numpy.zeros((d.d, d.d)))
assert_raises(AttributeError, getattr, d, "_log_sigma_sqrt_2pi")
assert_raises(AttributeError, getattr, d, "_inv_two_sigma")
def test_from_summaries(X, means, covs):
d = Normal(means, covs, covariance_type='full')
d.summarize(X)
d.from_summaries()
assert_raises(AssertionError, assert_array_almost_equal, means, d.means)
assert_array_almost_equal(d.covs, torch.cov(torch.tensor(X).T,
correction=0))
for param1, param2 in (means, covs), (None, None):
d = Normal(param1, param2, covariance_type='full')
d.summarize(X[:4])
d.from_summaries()
_test_fit_params(d, [1.525, 1.375, 1.55],
[[ 0.931875, 0.518125, 0.41125 ],
[ 0.518125, 0.626875, -0.04375 ],
[ 0.41125 , -0.04375 , 0.5025 ]])
d.summarize(X[4:])
d.from_summaries()
_test_fit_params(d, [3.666667, 1.4 , 1.466667],
[[1.742223, 0.373333, 2.235555],
[0.373333, 0.14 , 0.623333],
[2.235555, 0.623333, 3.215556]])
d = Normal(param1, param2, covariance_type='full')
d.summarize(X[:4])
d.summarize(X[4:])
d.from_summaries()
_test_fit_params(d, [2.442857, 1.385714, 1.514286],
[[2.402449, 0.469184, 1.149388],
[0.469184, 0.418367, 0.241633],
[1.149388, 0.241633, 1.666939]])
d = Normal(param1, param2, covariance_type='full')
d.summarize(X)
d.from_summaries()
_test_fit_params(d, [2.442857, 1.385714, 1.514286],
[[2.40245 , 0.469184, 1.149389],
[0.469184, 0.418367, 0.241633],
[1.149389, 0.241633, 1.666939]])
def test_from_summaries_weighted(X, w, means, covs):
for param in means, None:
d = Normal(means, covs, covariance_type='full')
d.summarize(X, sample_weight=w)
d.from_summaries()
_test_fit_params(d, [2.627273, 1.090909, 0.8],
[[2.038348, 0.360248, 0.533636],
[0.360248, 0.308099, 0.120909],
[0.533636, 0.120909, 1.187273]])
def test_from_summaries_null():
d = Normal([1., 2.], [[1., 0.], [0., 2.]], covariance_type='full')
d.from_summaries()
assert d.means[0] != 1 and d.means[1] != 2
assert d.covs[0, 0] != 1 and d.covs[1, 1] != 2
assert_array_almost_equal(d._w_sum, [0.0, 0.0])
assert_array_almost_equal(d._xw_sum, [0.0, 0.0])
assert_array_almost_equal(d._xxw_sum, [[0.0, 0.0], [0.0, 0.0]])
d = Normal([1., 2.], [[1., 0.], [0., 2.]], covariance_type='full', inertia=0.5)
d.from_summaries()
assert d.means[0] != 1 and d.means[1] != 2
assert d.covs[0, 0] != 1 and d.covs[1, 1] != 2
assert_array_almost_equal(d._w_sum, [0.0, 0.0])
assert_array_almost_equal(d._xw_sum, [0.0, 0.0])
assert_array_almost_equal(d._xxw_sum, [[0.0, 0.0], [0.0, 0.0]])
d = Normal([1., 2.], [[1., 0.], [0., 2.]], covariance_type='full', inertia=0.5, frozen=True)
d.from_summaries()
_test_fit_params(d, [1, 2], [[1., 0.], [0., 2.]])
def test_from_summaries_inertia(X, w, means, covs):
d = Normal(means, covs, covariance_type='full', inertia=0.3)
d.summarize(X[:4])
d.from_summaries()
_test_fit_params(d, [1.4275, 1.5025, 1.715],
[[0.742312, 0.392687, 0.287875],
[0.392687, 1.068812, 0.149375],
[0.287875, 0.149375, 0.71175 ]])
d.summarize(X[4:])
d.from_summaries()
_test_fit_params(d, [2.994917, 1.43075 , 1.541167],
[[1.44225 , 0.37914 , 1.651251],
[0.37914 , 0.418644, 0.481146],
[1.651251, 0.481146, 2.464414]])
d = Normal(means, covs, covariance_type='full', inertia=0.3)
d.summarize(X)
d.from_summaries()
_test_fit_params(d, [2.07, 1.51, 1.69],
[[1.771715, 0.358429, 0.804572],
[0.358429, 0.922857, 0.349143],
[0.804572, 0.349143, 1.526857]])
def test_from_summaries_weighted_inertia(X, w, means, covs):
d = Normal(means, covs, covariance_type='full', inertia=0.3)
d.summarize(X, sample_weight=w)
d.from_summaries()
_test_fit_params(d, [2.199091, 1.303636, 1.19],
[[1.516843, 0.282174, 0.373545],
[0.282174, 0.845669, 0.264636],
[0.373545, 0.264636, 1.191091]])
d = Normal(means, covs, covariance_type='full', inertia=1.0)
d.summarize(X[:4])
d.from_summaries()
_test_fit_params(d, means, covs)
d.summarize(X[4:])
d.from_summaries()
_test_fit_params(d, means, covs)
d = Normal(means, covs, covariance_type='full', inertia=1.0)
d.summarize(X)
d.from_summaries()
_test_fit_params(d, means, covs)
def test_from_summaries_frozen(X, w, means, covs):
d = Normal(means, covs, covariance_type='full', frozen=True)
d.summarize(X[:4])
assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xw_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xxw_sum,
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
d.from_summaries()
_test_fit_params(d, means, covs)
d.summarize(X[4:])
assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xw_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xxw_sum,
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
d.from_summaries()
_test_fit_params(d, means, covs)
d = Normal(means, covs, covariance_type='full', frozen=True)
d.summarize(X)
assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xw_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xxw_sum,
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
d.from_summaries()
_test_fit_params(d, means, covs)
d = Normal(means, covs, covariance_type='full', frozen=True)
d.summarize(X, sample_weight=w)
assert_array_almost_equal(d._w_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xw_sum, [0.0, 0.0, 0.0])
assert_array_almost_equal(d._xxw_sum,
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
d.from_summaries()
_test_fit_params(d, means, covs)
def test_from_summaries_dtypes(X, means,):
X = numpy.array(X)
X = X.astype(numpy.float32)
p = numpy.array(means, dtype=numpy.float32)
d = Normal(p, [p, p, p], covariance_type='full')
d.summarize(X)
d.from_summaries()
assert d.means.dtype == torch.float32
assert d.covs.dtype == torch.float32
p = numpy.array(means, dtype=numpy.float64)
d = Normal(p, [p, p, p], covariance_type='full')
d.summarize(X)
d.from_summaries()
assert d.means.dtype == torch.float64
assert d.covs.dtype == torch.float64
def test_from_summaries_raises():
assert_raises(AttributeError, Normal().from_summaries)
def test_fit(X, means, covs):
d = Normal(means, covs, covariance_type='full')
d.fit(X)
assert_raises(AssertionError, assert_array_almost_equal, means, d.means)
assert_array_almost_equal(d.covs, torch.cov(torch.tensor(X).T,
correction=0))
for param1, param2 in (means, covs), (None, None):
d = Normal(param1, param2, covariance_type='full')
d.fit(X[:4])
_test_fit_params(d, [1.525, 1.375, 1.55],
[[ 0.931875, 0.518125, 0.41125 ],
[ 0.518125, 0.626875, -0.04375 ],
[ 0.41125 , -0.04375 , 0.5025 ]])
d.fit(X[4:])
_test_fit_params(d, [3.666667, 1.4 , 1.466667],
[[1.742223, 0.373333, 2.235555],
[0.373333, 0.14 , 0.623333],
[2.235555, 0.623333, 3.215556]])
d = Normal(param1, param2, covariance_type='full')
d.fit(X)
_test_fit_params(d, [2.442857, 1.385714, 1.514286],
[[2.40245 , 0.469184, 1.149389],
[0.469184, 0.418367, 0.241633],
[1.149389, 0.241633, 1.666939]])
def test_fit_weighted(X, w, means, covs):
for param in means, None:
d = Normal(means, covs, covariance_type='full')
d.fit(X, sample_weight=w)
_test_fit_params(d, [2.627273, 1.090909, 0.8],
[[2.038348, 0.360248, 0.533636],
[0.360248, 0.308099, 0.120909],
[0.533636, 0.120909, 1.187273]])
def test_fit_chain(X):
d = Normal(covariance_type='full').fit(X[:4])
_test_fit_params(d, [1.525, 1.375, 1.55],
[[ 0.931875, 0.518125, 0.41125 ],
[ 0.518125, 0.626875, -0.04375 ],
[ 0.41125 , -0.04375 , 0.5025 ]])
d.fit(X[4:])
_test_fit_params(d, [3.666667, 1.4 , 1.466667],
[[1.742223, 0.373333, 2.235555],
[0.373333, 0.14 , 0.623333],
[2.235555, 0.623333, 3.215556]])
d = Normal(covariance_type='full').fit(X)
_test_fit_params(d, [2.442857, 1.385714, 1.514286],
[[2.40245 , 0.469184, 1.149389],
[0.469184, 0.418367, 0.241633],
[1.149389, 0.241633, 1.666939]])
def test_fit_dtypes(X, means):
X = numpy.array(X)
X = X.astype(numpy.float32)
p = numpy.array(means, dtype=numpy.float32)
d = Normal(p, [p, p, p], covariance_type='full')
d.fit(X)
assert d.means.dtype == torch.float32
assert d.covs.dtype == torch.float32
p = numpy.array(means, dtype=numpy.float64)
d = Normal(p, [p, p, p], covariance_type='full')
d.fit(X)
assert d.means.dtype == torch.float64
assert d.covs.dtype == torch.float64
def test_fit_raises(X, w, means):
_test_raises(Normal(means, covariance_type='full'), "fit", X, w=w,
min_value=MIN_VALUE, max_value=MAX_VALUE)
_test_raises(Normal(covariance_type='full'), "fit", X, w=w,
min_value=MIN_VALUE, max_value=MAX_VALUE)
_test_raises(Normal([VALID_VALUE], covariance_type='full'), "fit", X, w=w,
min_value=MIN_VALUE, max_value=MAX_VALUE)
def test_serialization(X):
d = Normal(covariance_type='full').fit(X[:4])
d.summarize(X[4:])
means = [1.525, 1.375, 1.55 ]
covs = [[ 0.931875, 0.518125, 0.41125 ],
[ 0.518125, 0.626875, -0.04375 ],
[ 0.41125 , -0.04375 , 0.5025 ]]
assert_array_almost_equal(d.means, means)
assert_array_almost_equal(d.covs, covs)
torch.save(d, ".pytest.torch")
d2 = torch.load(".pytest.torch")
os.system("rm .pytest.torch")
assert_array_almost_equal(d2.means, means)
assert_array_almost_equal(d2.covs, covs)
assert_array_almost_equal(d2._w_sum, [3., 3., 3.])
assert_array_almost_equal(d2._xw_sum, [11. , 4.2, 4.4])
assert_array_almost_equal(d2._xxw_sum,
[[45.56, 16.52, 22.84],
[16.52, 6.3 , 8.03],
[22.84, 8.03, 16.1 ]])
assert_array_almost_equal(d.log_probability(X), d2.log_probability(X))