Here are the examples of the python api numpy.newaxis taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
145 Examples
3
View Complete Implementation : _base.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def softmax(X):
"""Compute the K-way softmax function inplace.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
Returns
-------
X_new : {array-like, sparse matrix}, shape (n_samples, n_features)
The transformed data.
"""
tmp = X - X.max(axis=1)[:, np.newaxis]
np.exp(tmp, out=X)
X /= X.sum(axis=1)[:, np.newaxis]
return X
3
View Complete Implementation : quiver.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def set_offsets(self, xy):
"""
Set the offsets for the barb polygons. This saves the offsets pasted
in and actually sets version masked as appropriate for the existing
U/V data. *offsets* should be a sequence.
ACCEPTS: sequence of pairs of floats
"""
self.x = xy[:, 0]
self.y = xy[:, 1]
x, y, u, v = delete_masked_points(self.x.ravel(), self.y.ravel(),
self.u, self.v)
_check_consistent_shapes(x, y, u, v)
xy = np.hstack((x[:, np.newaxis], y[:, np.newaxis]))
mcollections.PolyCollection.set_offsets(self, xy)
self.stale = True
3
View Complete Implementation : test_coordinate_descent.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_1d_multioutput_enet_and_mulsatask_enet_cv():
X, y, _, _ = build_dataset(n_features=10)
y = y[:, np.newaxis]
clf = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf.fit(X, y[:, 0])
clf1 = MulsataskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf1.fit(X, y)
astert_almost_equal(clf.l1_ratio_, clf1.l1_ratio_)
astert_almost_equal(clf.alpha_, clf1.alpha_)
astert_almost_equal(clf.coef_, clf1.coef_[0])
astert_almost_equal(clf.intercept_, clf1.intercept_[0])
3
View Complete Implementation : test_base.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_linear_regression_sparse(random_state=0):
# Test that linear regression also works with sparse data
random_state = check_random_state(random_state)
for i in range(10):
n = 100
X = sparse.eye(n, n)
beta = random_state.rand(n)
y = X * beta[:, np.newaxis]
ols = LinearRegression()
ols.fit(X, y.ravel())
astert_array_almost_equal(beta, ols.coef_ + ols.intercept_)
astert_array_almost_equal(ols.predict(X) - y.ravel(), 0)
3
View Complete Implementation : test_base.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_rescale_data():
n_samples = 200
n_features = 2
sample_weight = 1.0 + rng.rand(n_samples)
X = rng.rand(n_samples, n_features)
y = rng.rand(n_samples)
rescaled_X, rescaled_y = _rescale_data(X, y, sample_weight)
rescaled_X2 = X * np.sqrt(sample_weight)[:, np.newaxis]
rescaled_y2 = y * np.sqrt(sample_weight)
astert_array_almost_equal(rescaled_X, rescaled_X2)
astert_array_almost_equal(rescaled_y, rescaled_y2)
3
View Complete Implementation : linear_assignment_.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def _step1(state):
"""Steps 1 and 2 in the Wikipedia page."""
# Step1: For each row of the matrix, find the smallest element and
# subtract it from every element in its row.
state.C -= state.C.min(axis=1)[:, np.newaxis]
# Step2: Find a zero (Z) in the resulting matrix. If there is no
# starred zero in its row or column, star Z. Repeat for each element
# in the matrix.
for i, j in zip(*np.where(state.C == 0)):
if state.col_uncovered[j] and state.row_uncovered[i]:
state.marked[i, j] = 1
state.col_uncovered[j] = False
state.row_uncovered[i] = False
state._clear_covers()
return _step3
3
View Complete Implementation : test_dict_learning.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_dict_learning_nonzero_coefs():
n_components = 4
dico = DictionaryLearning(n_components, transform_algorithm='lars',
transform_n_nonzero_coefs=3, random_state=0)
code = dico.fit(X).transform(X[np.newaxis, 1])
astert_true(len(np.flatnonzero(code)) == 3)
dico.set_params(transform_algorithm='omp')
code = dico.transform(X[np.newaxis, 1])
astert_equal(len(np.flatnonzero(code)), 3)
3
View Complete Implementation : calibration.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def _preproc(self, X):
n_clastes = len(self.clastes_)
if hasattr(self.base_estimator, "decision_function"):
df = self.base_estimator.decision_function(X)
if df.ndim == 1:
df = df[:, np.newaxis]
elif hasattr(self.base_estimator, "predict_proba"):
df = self.base_estimator.predict_proba(X)
if n_clastes == 2:
df = df[:, 1:]
else:
raise RuntimeError('clastifier has no decision_function or '
'predict_proba method.')
idx_pos_clast = self.label_encoder_.\
transform(self.base_estimator.clastes_)
return df, idx_pos_clast
3
View Complete Implementation : bayesian_mixture.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def _estimate_means(self, nk, xk):
"""Estimate the parameters of the Gaussian distribution.
Parameters
----------
nk : array-like, shape (n_components,)
xk : array-like, shape (n_components, n_features)
"""
self.mean_precision_ = self.mean_precision_prior_ + nk
self.means_ = ((self.mean_precision_prior_ * self.mean_prior_ +
nk[:, np.newaxis] * xk) /
self.mean_precision_[:, np.newaxis])
3
View Complete Implementation : test_weight_boosting.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def test_importances():
# Check variable importances.
X, y = datasets.make_clastification(n_samples=2000,
n_features=10,
n_informative=3,
n_redundant=0,
n_repeated=0,
shuffle=False,
random_state=1)
for alg in ['SAMME', 'SAMME.R']:
clf = AdaBoostClastifier(algorithm=alg)
clf.fit(X, y)
importances = clf.feature_importances_
astert_equal(importances.shape[0], 10)
astert_equal((importances[:3, np.newaxis] >= importances[3:]).all(),
True)