Here are the examples of the python api numpy.random.mtrand.RandomState taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
106 Examples
3
Source : test_nmf.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_initialize_nn_output():
# Test that initialization does not return negative values
rng = np.random.mtrand.RandomState(42)
data = np.abs(rng.randn(10, 10))
for init in ('random', 'nndsvd', 'nndsvda', 'nndsvdar'):
W, H = nmf._initialize_nmf(data, 10, init=init, random_state=0)
assert_false((W < 0).any() or (H < 0).any())
def test_parameter_checking():
3
Source : test_nmf.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_initialize_close():
# Test NNDSVD error
# Test that _initialize_nmf error is less than the standard deviation of
# the entries in the matrix.
rng = np.random.mtrand.RandomState(42)
A = np.abs(rng.randn(10, 10))
W, H = nmf._initialize_nmf(A, 10, init='nndsvd')
error = linalg.norm(np.dot(W, H) - A)
sdev = linalg.norm(A - A.mean())
assert_true(error < = sdev)
def test_initialize_variants():
3
Source : test_nmf.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_initialize_variants():
# Test NNDSVD variants correctness
# Test that the variants 'nndsvda' and 'nndsvdar' differ from basic
# 'nndsvd' only where the basic version has zeros.
rng = np.random.mtrand.RandomState(42)
data = np.abs(rng.randn(10, 10))
W0, H0 = nmf._initialize_nmf(data, 10, init='nndsvd')
Wa, Ha = nmf._initialize_nmf(data, 10, init='nndsvda')
War, Har = nmf._initialize_nmf(data, 10, init='nndsvdar',
random_state=0)
for ref, evl in ((W0, Wa), (W0, War), (H0, Ha), (H0, Har)):
assert_almost_equal(evl[ref != 0], ref[ref != 0])
# ignore UserWarning raised when both solver='mu' and init='nndsvd'
@ignore_warnings(category=UserWarning)
3
Source : test_nmf.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_nmf_fit_close():
rng = np.random.mtrand.RandomState(42)
# Test that the fit is not too far away
for solver in ('cd', 'mu'):
pnmf = NMF(5, solver=solver, init='nndsvdar', random_state=0,
max_iter=600)
X = np.abs(rng.randn(6, 5))
assert_less(pnmf.fit(X).reconstruction_err_, 0.1)
def test_nmf_transform():
3
Source : test_nmf.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_nmf_transform():
# Test that NMF.transform returns close values
rng = np.random.mtrand.RandomState(42)
A = np.abs(rng.randn(6, 5))
for solver in ['cd', 'mu']:
m = NMF(solver=solver, n_components=3, init='random',
random_state=0, tol=1e-5)
ft = m.fit_transform(A)
t = m.transform(A)
assert_array_almost_equal(ft, t, decimal=2)
def test_nmf_transform_custom_init():
3
Source : test_nmf.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_n_components_greater_n_features():
# Smoke test for the case of more components than features.
rng = np.random.mtrand.RandomState(42)
A = np.abs(rng.randn(30, 10))
NMF(n_components=15, random_state=0, tol=1e-2).fit(A)
def test_nmf_sparse_input():
3
Source : test_nmf.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_nmf_sparse_transform():
# Test that transform works on sparse data. Issue #2124
rng = np.random.mtrand.RandomState(42)
A = np.abs(rng.randn(3, 2))
A[1, 1] = 0
A = csc_matrix(A)
for solver in ('cd', 'mu'):
model = NMF(solver=solver, random_state=0, n_components=2,
max_iter=400)
A_fit_tr = model.fit_transform(A)
A_tr = model.transform(A)
assert_array_almost_equal(A_fit_tr, A_tr, decimal=1)
def test_non_negative_factorization_consistency():
3
Source : rng.py
with GNU General Public License v3.0
from Artikash
with GNU General Public License v3.0
from Artikash
def __init__(self, seed, dist=None):
if seed < = 0:
self._rng = mt.RandomState()
elif seed > 0:
self._rng = mt.RandomState(seed)
if dist is None:
dist = default_distribution
if not isinstance(dist, Distribution):
raise error("Not a distribution object")
self._dist = dist
def ranf(self):
3
Source : decision_tree.py
with Apache License 2.0
from bsc-wdc
with Apache License 2.0
from bsc-wdc
def _sample_selection(n_samples, y_targets, bootstrap, seed):
if bootstrap:
random_state = RandomState(seed)
selection = random_state.choice(
n_samples, size=n_samples, replace=True
)
selection.sort()
return selection, y_targets[selection]
else:
return np.arange(n_samples), y_targets
def _feature_selection(untried_indices, m_try, random_state):
3
Source : decision_tree.py
with Apache License 2.0
from bsc-wdc
with Apache License 2.0
from bsc-wdc
def _split_node_using_features(
sample, n_features, y_s, n_classes, m_try, features_file, seed
):
features_mmap = np.load(features_file, mmap_mode="r", allow_pickle=False)
random_state = RandomState(seed)
return _compute_split(
sample, n_features, y_s, n_classes, m_try, features_mmap, random_state
)
@constraint(computing_units="${ComputingUnits}")
3
Source : decision_tree.py
with Apache License 2.0
from bsc-wdc
with Apache License 2.0
from bsc-wdc
def _split_node(sample, n_features, y_s, n_classes, m_try, samples_file, seed):
features_mmap = np.load(samples_file, mmap_mode="r", allow_pickle=False).T
random_state = RandomState(seed)
return _compute_split(
sample, n_features, y_s, n_classes, m_try, features_mmap, random_state
)
def _compute_split(
3
Source : sampler.py
with MIT License
from CIRADA-Tools
with MIT License
from CIRADA-Tools
def __init__(self, dim, lnprobfn, args=[], kwargs={}):
self.dim = dim
self.lnprobfn = lnprobfn
self.args = args
self.kwargs = kwargs
# This is a random number generator that we can easily set the state
# of without affecting the numpy-wide generator
self._random = np.random.mtrand.RandomState()
self.reset()
@property
3
Source : test_nmf.py
with Apache License 2.0
from dashanji
with Apache License 2.0
from dashanji
def test_initialize_nn_output():
# Test that initialization does not return negative values
rng = np.random.mtrand.RandomState(42)
data = np.abs(rng.randn(10, 10))
for init in ('random', 'nndsvd', 'nndsvda', 'nndsvdar'):
W, H = nmf._initialize_nmf(data, 10, init=init, random_state=0)
assert not ((W < 0).any() or (H < 0).any())
def test_parameter_checking():
3
Source : test_nmf.py
with Apache License 2.0
from dashanji
with Apache License 2.0
from dashanji
def test_initialize_close():
# Test NNDSVD error
# Test that _initialize_nmf error is less than the standard deviation of
# the entries in the matrix.
rng = np.random.mtrand.RandomState(42)
A = np.abs(rng.randn(10, 10))
W, H = nmf._initialize_nmf(A, 10, init='nndsvd')
error = linalg.norm(np.dot(W, H) - A)
sdev = linalg.norm(A - A.mean())
assert error < = sdev
def test_initialize_variants():
3
Source : test_nmf.py
with Apache License 2.0
from dashanji
with Apache License 2.0
from dashanji
def test_nmf_fit_close(solver):
rng = np.random.mtrand.RandomState(42)
# Test that the fit is not too far away
pnmf = NMF(5, solver=solver, init='nndsvdar', random_state=0,
max_iter=600)
X = np.abs(rng.randn(6, 5))
assert pnmf.fit(X).reconstruction_err_ < 0.1
@pytest.mark.parametrize('solver', ('cd', 'mu'))
3
Source : test_nmf.py
with Apache License 2.0
from dashanji
with Apache License 2.0
from dashanji
def test_nmf_transform(solver):
# Test that NMF.transform returns close values
rng = np.random.mtrand.RandomState(42)
A = np.abs(rng.randn(6, 5))
m = NMF(solver=solver, n_components=3, init='random',
random_state=0, tol=1e-5)
ft = m.fit_transform(A)
t = m.transform(A)
assert_array_almost_equal(ft, t, decimal=2)
def test_nmf_transform_custom_init():
3
Source : simulation.py
with MIT License
from deeplearningbrasil
with MIT License
from deeplearningbrasil
def __init__(self, reward_model: nn.Module, epsilon: float = 0.1, seed: int = 42) -> None:
super().__init__(reward_model)
self._epsilon = epsilon
self._rng = RandomState(seed)
def _select_idx(
3
Source : bandit.py
with MIT License
from deeplearningbrasil
with MIT License
from deeplearningbrasil
def __init__(
self,
reward_model: nn.Module,
explore_rounds: int = 500,
decay_rate: float = 0.0026456,
seed: int = 42,
) -> None:
super().__init__(reward_model)
self._init_explore_rounds = explore_rounds
self._explore_rounds = explore_rounds
self._exploit_rounds = explore_rounds
self._decay_rate = decay_rate
self._rng = RandomState(seed)
self._t = 0
self._te = 0
self.exploring = True
def _update_state(self):
3
Source : bandit.py
with MIT License
from deeplearningbrasil
with MIT License
from deeplearningbrasil
def __init__(
self,
reward_model: nn.Module,
epsilon: float = 0.1,
epsilon_decay: float = 1.0,
seed: int = 42,
) -> None:
super().__init__(reward_model)
self._epsilon = epsilon
self._rng = RandomState(seed)
self._epsilon_decay = epsilon_decay
def _compute_prob(
3
Source : bandit.py
with MIT License
from deeplearningbrasil
with MIT License
from deeplearningbrasil
def __init__(
self,
reward_model: nn.Module,
exploration_threshold: float = 0.8,
decay_rate: float = 0.0010391,
seed: int = 42,
) -> None:
super().__init__(reward_model)
self._init_exploration_threshold = exploration_threshold
self._exploration_threshold = exploration_threshold
self._decay_rate = decay_rate
self._rng = RandomState(seed)
self._t = 0
def _compute_prob(
3
Source : bandit.py
with MIT License
from deeplearningbrasil
with MIT License
from deeplearningbrasil
def __init__(
self,
reward_model: nn.Module,
window_size: int = 500,
exploration_threshold: float = 0.5,
percentile=35,
percentile_decay: float = 1.0,
seed: int = 42,
) -> None:
super().__init__(reward_model)
self._window_size = window_size
self._initial_exploration_threshold = exploration_threshold
self._percentile_decay = percentile_decay
self._best_arm_history = {} # We save a deque for each pos
self._rng = RandomState(seed)
self._percentile = percentile
self._t = 0
def _compute_prob(
3
Source : bandit.py
with MIT License
from deeplearningbrasil
with MIT License
from deeplearningbrasil
def __init__(
self,
reward_model: nn.Module,
logit_multiplier: float = 1.0,
reverse_sigmoid: bool = True,
seed: int = 42,
) -> None:
super().__init__(reward_model)
self._logit_multiplier = logit_multiplier
self._rng = RandomState(seed)
self._reverse_sigmoid = reverse_sigmoid
def _softmax(self, x: np.ndarray) -> np.ndarray:
3
Source : epsilon_greedy_agent.py
with MIT License
from falox
with MIT License
from falox
def __init__(self, seed, epsilon):
self.name = "epsilon-Greedy Agent"
self.np_random = RandomState(seed)
self.epsilon = epsilon
def act(self, observation, reward, done):
3
Source : softmax_agent.py
with MIT License
from falox
with MIT License
from falox
def __init__(self, seed, beta, max_impressions):
self.name = "Softmax Agent"
self.np_random = RandomState(seed)
self.beta = beta
self.max_impressions = max_impressions
def act(self, observation, reward, done):
3
Source : ucb1_agent.py
with MIT License
from falox
with MIT License
from falox
def __init__(self, action_space, seed, c, max_impressions):
self.name = "UCB1 Agent"
self.values = [0.00] * action_space.n
self.np_random = RandomState(seed)
self.c = c
self.max_impressions = max_impressions
self.prev_action = None
def act(self, observation, reward, done):
3
Source : test_nmf.py
with GNU General Public License v3.0
from gustavowillam
with GNU General Public License v3.0
from gustavowillam
def test_nmf_fit_close(solver, regularization):
rng = np.random.mtrand.RandomState(42)
# Test that the fit is not too far away
pnmf = NMF(5, solver=solver, init='nndsvdar', random_state=0,
regularization=regularization, max_iter=600)
X = np.abs(rng.randn(6, 5))
assert pnmf.fit(X).reconstruction_err_ < 0.1
@pytest.mark.parametrize('solver', ('cd', 'mu'))
3
Source : test_nmf.py
with GNU General Public License v3.0
from gustavowillam
with GNU General Public License v3.0
from gustavowillam
def test_nmf_transform(solver, regularization):
# Test that NMF.transform returns close values
rng = np.random.mtrand.RandomState(42)
A = np.abs(rng.randn(6, 5))
m = NMF(solver=solver, n_components=3, init='random',
regularization=regularization, random_state=0, tol=1e-5)
ft = m.fit_transform(A)
t = m.transform(A)
assert_array_almost_equal(ft, t, decimal=2)
def test_nmf_transform_custom_init():
3
Source : test_nmf.py
with GNU General Public License v3.0
from gustavowillam
with GNU General Public License v3.0
from gustavowillam
def test_n_components_greater_n_features():
# Smoke test for the case of more components than features.
rng = np.random.mtrand.RandomState(42)
A = np.abs(rng.randn(30, 10))
# FIXME : should be removed in 1.1
init = 'random'
NMF(n_components=15, random_state=0, tol=1e-2, init=init).fit(A)
@pytest.mark.parametrize('solver', ['cd', 'mu'])
3
Source : test_nmf.py
with GNU General Public License v3.0
from gustavowillam
with GNU General Public License v3.0
from gustavowillam
def test_nmf_sparse_transform():
# Test that transform works on sparse data. Issue #2124
rng = np.random.mtrand.RandomState(42)
A = np.abs(rng.randn(3, 2))
A[1, 1] = 0
A = csc_matrix(A)
for solver in ('cd', 'mu'):
model = NMF(solver=solver, random_state=0, n_components=2,
max_iter=400, init='nndsvd')
A_fit_tr = model.fit_transform(A)
A_tr = model.transform(A)
assert_array_almost_equal(A_fit_tr, A_tr, decimal=1)
@pytest.mark.parametrize('init', ['random', 'nndsvd'])
3
Source : test_nmf.py
with GNU General Public License v3.0
from gustavowillam
with GNU General Public License v3.0
from gustavowillam
def test_init_default_deprecation():
# Test FutureWarning on init default
msg = (r"The 'init' value, when 'init=None' and "
r"n_components is less than n_samples and "
r"n_features, will be changed from 'nndsvd' to "
r"'nndsvda' in 1.1 \(renaming of 0.26\).")
rng = np.random.mtrand.RandomState(42)
A = np.abs(rng.randn(6, 5))
with pytest.warns(FutureWarning, match=msg):
nmf._initialize_nmf(A, 3)
with pytest.warns(FutureWarning, match=msg):
NMF().fit(A)
with pytest.warns(FutureWarning, match=msg):
non_negative_factorization(A)
3
Source : test_nmf.py
with GNU General Public License v3.0
from HHHHhgqcdxhg
with GNU General Public License v3.0
from HHHHhgqcdxhg
def test_nmf_fit_close(solver):
rng = np.random.mtrand.RandomState(42)
# Test that the fit is not too far away
pnmf = NMF(5, solver=solver, init='nndsvdar', random_state=0,
max_iter=600)
X = np.abs(rng.randn(6, 5))
assert_less(pnmf.fit(X).reconstruction_err_, 0.1)
@pytest.mark.parametrize('solver', ('cd', 'mu'))
3
Source : test_metrics.py
with MIT License
from idanmoradarthas
with MIT License
from idanmoradarthas
def test_plot_metric_growth_per_labeled_instances_given_random_state():
plot_metric_growth_per_labeled_instances(x_train, y_train, x_test, y_test,
{"DecisionTreeClassifier": DecisionTreeClassifier(random_state=0),
"RandomForestClassifier": RandomForestClassifier(random_state=0,
n_estimators=5)},
random_state=RandomState(5))
result_path = Path(__file__).parents[0].absolute().joinpath("result_images").joinpath(
"test_metrics").joinpath("test_plot_metric_growth_per_labeled_instances_given_random_state.png")
pyplot.savefig(str(result_path))
baseline_path = Path(__file__).parents[0].absolute().joinpath("baseline_images").joinpath(
"test_metrics").joinpath("test_plot_metric_growth_per_labeled_instances_given_random_state.png")
pyplot.cla()
pyplot.close(pyplot.gcf())
compare_images_from_paths(str(baseline_path), str(result_path))
def test_plot_metric_growth_per_labeled_instances_exists_ax():
3
Source : codecs.py
with GNU Affero General Public License v3.0
from nccgroup
with GNU Affero General Public License v3.0
from nccgroup
def encode(cls, obj):
import numpy as np
assert type(obj) == np.random.mtrand.RandomState
init_args = obj.__reduce__()[1]
state = obj.__getstate__()
return {
'__mlspl_type': [type(obj).__module__, type(obj).__name__],
'init_args': init_args,
'state': state
}
@classmethod
3
Source : codecs.py
with GNU Affero General Public License v3.0
from nccgroup
with GNU Affero General Public License v3.0
from nccgroup
def decode(cls, obj):
from numpy.random.mtrand import RandomState
init_args = obj['init_args']
state = obj['state']
t = RandomState(*init_args)
t.__setstate__(state)
return t
class SparseMatrixCodec(BaseCodec):
3
Source : epsilon_greedy.py
with MIT License
from olivierjeunen
with MIT License
from olivierjeunen
def __init__(self, config, agent):
super(EpsilonGreedy, self).__init__(config)
self.agent = agent
self.rng = RandomState(self.config.random_seed)
def train(self, observation, action, reward, done = False):
3
Source : abstract.py
with MIT License
from olivierjeunen
with MIT License
from olivierjeunen
def reset_random_seed(self, epoch = 0):
# Initialize Random State.
assert (self.config.random_seed is not None)
self.rng = RandomState(self.config.random_seed + epoch)
def init_gym(self, args):
3
Source : normal_time_generator.py
with MIT License
from olivierjeunen
with MIT License
from olivierjeunen
def __init__(self, config):
super(NormalTimeGenerator, self).__init__(config)
self.current_time = 0
if not hasattr(self.config, 'normal_time_mu'):
self.normal_time_mu = 0
else:
self.normal_time_mu = self.config.normal_time_mu
if not hasattr(self.config, 'normal_time_sigma'):
self.normal_time_sigma = 1
else:
self.normal_time_sigma = self.config.normal_time_sigma
self.rng = RandomState(config.random_seed)
def new_time(self):
3
Source : test_sampling_spn.py
with GNU General Public License v3.0
from probabilistic-learning
with GNU General Public License v3.0
from probabilistic-learning
def test_induced_trees_correct_parameters(self):
node_1_2_2 = Leaf(0)
node_1_2_1 = Leaf(1)
node_1_1 = Leaf([0, 1])
node_1_2 = node_1_2_1 * node_1_2_2
spn = 0.1 * node_1_1 + 0.9 * node_1_2
node_1_2.id = 0
rand_gen = RandomState(1234)
with self.assertRaises(AssertionError):
sample_induced_trees(spn, rand_gen.rand(10, 3), rand_gen)
assign_ids(spn)
node_1_2_2.id += 1
with self.assertRaises(AssertionError):
sample_induced_trees(spn, rand_gen.rand(10, 3), rand_gen)
def test_induced_trees(self):
3
Source : test_randomstate_regression.py
with MIT License
from shreyasgaonkar
with MIT License
from shreyasgaonkar
def test_call_within_randomstate(self):
# Check that custom RandomState does not call into global state
m = random.RandomState()
res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
for i in range(3):
random.seed(i)
m.seed(4321)
# If m.state is not honored, the result will change
assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
def test_multivariate_normal_size_types(self):
3
Source : benchmark_cmf.py
with MIT License
from smn-ailab
with MIT License
from smn-ailab
def dense_cmf_benchmark(solver):
rng = np.random.mtrand.RandomState(42)
X = np.abs(rng.randn(2000, 150))
Y = np.abs(rng.randn(150, 10))
model = CMF(n_components=10, solver=solver,
random_state=42, max_iter=10)
U, V, Z = model.fit_transform(X, Y)
def dense_cmf_with_logits_benchmark():
3
Source : benchmark_cmf.py
with MIT License
from smn-ailab
with MIT License
from smn-ailab
def dense_cmf_with_logits_benchmark():
rng = np.random.mtrand.RandomState(42)
X = np.abs(rng.randn(2000, 150))
Y = np.abs(rng.randn(150, 10))
model = CMF(n_components=10, solver="newton",
random_state=42, max_iter=10)
U, V, Z = model.fit_transform(X, Y)
def sparse_cmf_benchmark(solver):
3
Source : benchmark_cmf.py
with MIT License
from smn-ailab
with MIT License
from smn-ailab
def sparse_cmf_benchmark(solver):
rng = np.random.mtrand.RandomState(42)
X = np.abs(rng.randn(2000, 150))
X[:1000, 2 * np.arange(10) + 100] = 0
X[1000:, 2 * np.arange(10)] = 0
X_sparse = SP(X)
Y = np.abs(rng.randn(150, 10))
model = CMF(n_components=10, solver=solver,
random_state=42, max_iter=10)
U, V, Z = model.fit_transform(X_sparse, Y)
def sparse_cmf_with_logits_benchmark(sample_ratio):
3
Source : benchmark_cmf.py
with MIT License
from smn-ailab
with MIT License
from smn-ailab
def sparse_cmf_with_logits_benchmark(sample_ratio):
rng = np.random.mtrand.RandomState(42)
X = np.abs(rng.randn(2000, 150))
X[:1000, 2 * np.arange(10) + 100] = 0
X[1000:, 2 * np.arange(10)] = 0
X_sparse = SP(X)
Y = expit(rng.randn(150, 10))
model = CMF(n_components=10, solver="newton",
random_state=42, sg_sample_ratio=sample_ratio,
max_iter=10)
U, V, Z = model.fit_transform(X_sparse, Y)
if __name__ == "__main__":
3
Source : test_cmf.py
with MIT License
from smn-ailab
with MIT License
from smn-ailab
def test_fit_close(solver):
rng = np.random.mtrand.RandomState(42)
# Test that the fit is not too far away
for rndm_state in [0]:
pnmf = CMF(n_components=5, solver=solver, x_init='nndsvdar', y_init='nndsvdar',
random_state=rndm_state, max_iter=1000)
X = np.abs(rng.randn(6, 5))
Y = np.abs(rng.randn(5, 6))
assert_less(pnmf.fit(X, Y).reconstruction_err_, 0.1)
def test_transform_custom_init():
3
Source : test_cmf.py
with MIT License
from smn-ailab
with MIT License
from smn-ailab
def test_n_components_greater_n_features():
# Smoke test for the case of more components than features.
rng = np.random.mtrand.RandomState(42)
X = np.abs(rng.randn(30, 10))
Y = np.abs(rng.randn(10, 15))
CMF(n_components=15, random_state=0, tol=1e-2).fit(X, Y)
@pytest.mark.parametrize("solver", solvers)
3
Source : test_cmf.py
with MIT License
from smn-ailab
with MIT License
from smn-ailab
def test_recover_low_rank_matrix(solver):
rng = np.random.mtrand.RandomState(42)
# Test that the fit is not too far away
pnmf = CMF(5, solver=solver, x_init='nndsvdar', y_init='nndsvdar',
random_state=0, max_iter=1000)
U = np.abs(rng.randn(10, 5))
V = np.abs(rng.randn(8, 5))
Z = np.abs(rng.randn(6, 5))
X = np.dot(U, V.T)
Y = np.dot(V, Z.T)
assert_less(pnmf.fit(X, Y).reconstruction_err_, 1.0)
@ignore_warnings(category=ConvergenceWarning)
3
Source : test_cmf.py
with MIT License
from smn-ailab
with MIT License
from smn-ailab
def test_logit_link_optimization():
n_components = 5
rng = np.random.mtrand.RandomState(42)
X = 1 / (1 + np.exp(-rng.randn(6, 5)))
Y = 1 / (1 + np.exp(-rng.randn(5, 4)))
model = CMF(n_components=n_components, solver="newton",
l2_reg=0., random_state=42, x_link="logit", y_link="logit",
U_non_negative=False, V_non_negative=False, Z_non_negative=False)
U, V, Z = model.fit_transform(X, Y)
assert_less(model.reconstruction_err_, 0.1)
def test_logit_link_non_negative_optimization():
3
Source : test_cmf.py
with MIT License
from smn-ailab
with MIT License
from smn-ailab
def test_logit_link_non_negative_optimization():
# Test if the logit link function works with a non-negative counterpart
n_components = 5
rng = np.random.mtrand.RandomState(42)
X = rng.randn(6, 5)
X[X < 0] = 0
Y = 1 / (1 + np.exp(-rng.randn(5, 4)))
model = CMF(n_components=n_components, solver="newton",
l2_reg=0., random_state=42, y_link="logit",
U_non_negative=True, V_non_negative=True, Z_non_negative=False,
hessian_pertubation=0.2, max_iter=1000)
U, V, Z = model.fit_transform(X, Y)
assert_less(model.reconstruction_err_, 0.1)
@pytest.mark.parametrize("solver", solvers)
3
Source : test_cmf.py
with MIT License
from smn-ailab
with MIT License
from smn-ailab
def test_stochastic_newton_solver():
rng = np.random.mtrand.RandomState(42)
model = CMF(n_components=5, solver="newton", x_init='svd', y_init='svd',
U_non_negative=False, V_non_negative=False, Z_non_negative=False, alpha=0.5,
sg_sample_ratio=0.5, random_state=0, max_iter=1000)
X = rng.randn(6, 5)
Y = rng.randn(5, 6)
assert_less(model.fit(X, Y).reconstruction_err_, 0.1)
def test_stochastic_newton_solver_sparse_input_close():
3
Source : test_cmf.py
with MIT License
from smn-ailab
with MIT License
from smn-ailab
def test_stochastic_newton_solver_sparse_input_close():
rng = np.random.mtrand.RandomState(42)
model = CMF(n_components=5, solver="newton", x_init='svd', y_init='svd',
U_non_negative=False, V_non_negative=False, Z_non_negative=False, alpha=0.5,
sg_sample_ratio=0.5, random_state=0, max_iter=1000)
A = rng.randn(6, 5)
B = rng.randn(5, 6)
A_sparse = csr_matrix(A)
B_sparse = csr_matrix(B)
assert_less(model.fit(A_sparse, B_sparse).reconstruction_err_, 0.1)
def test_stochastic_newton_solver_sparse_input():
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