本文整理汇总了Python中sklearn.utils.extmath.row_norms方法的典型用法代码示例。如果您正苦于以下问题：Python extmath.row_norms方法的具体用法？Python extmath.row_norms怎么用？Python extmath.row_norms使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块sklearn.utils.extmath的用法示例。

在下文中一共展示了extmath.row_norms方法的14个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: predict

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def predict(self, X):

"""Predict the closest cluster each sample in X belongs to.

In the vector quantization literature, `cluster_centers_` is called

the code book and each value returned by `predict` is the index of

the closest code in the code book.

Parameters

----------

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

New data to predict.

Returns

-------

labels : array, shape [n_samples,]

Index of the cluster each sample belongs to.

"""

check_is_fitted(self, 'cluster_centers_')

X = self._check_test_data(X)

x_squared_norms = row_norms(X, squared=True)

return _labels_inertia(X, x_squared_norms, self.cluster_centers_)[0]

开发者ID:ndanielsen，项目名称:Same-Size-K-Means，代码行数:21，

示例2: score

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def score(self, X, y=None):

"""Opposite of the value of X on the K-means objective.

Parameters

----------

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

New data.

Returns

-------

score : float

Opposite of the value of X on the K-means objective.

"""

check_is_fitted(self, 'cluster_centers_')

X = self._check_test_data(X)

x_squared_norms = row_norms(X, squared=True)

return -_labels_inertia(X, x_squared_norms, self.cluster_centers_)[1]

开发者ID:ndanielsen，项目名称:Same-Size-K-Means，代码行数:18，

示例3: test_row_norms

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def test_row_norms():

X = np.random.RandomState(42).randn(100, 100)

for dtype in (np.float32, np.float64):

if dtype is np.float32:

precision = 4

else:

precision = 5

X = X.astype(dtype)

sq_norm = (X ** 2).sum(axis=1)

assert_array_almost_equal(sq_norm, row_norms(X, squared=True),

precision)

assert_array_almost_equal(np.sqrt(sq_norm), row_norms(X), precision)

Xcsr = sparse.csr_matrix(X, dtype=dtype)

assert_array_almost_equal(sq_norm, row_norms(Xcsr, squared=True),

precision)

assert_array_almost_equal(np.sqrt(sq_norm), row_norms(Xcsr), precision)

开发者ID:alvarobartt，项目名称:twitter-stock-recommendation，代码行数:21，

示例4: test_row_norms

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def test_row_norms(dtype):

X = np.random.RandomState(42).randn(100, 100)

if dtype is np.float32:

precision = 4

else:

precision = 5

X = X.astype(dtype, copy=False)

sq_norm = (X ** 2).sum(axis=1)

assert_array_almost_equal(sq_norm, row_norms(X, squared=True),

precision)

assert_array_almost_equal(np.sqrt(sq_norm), row_norms(X), precision)

for csr_index_dtype in [np.int32, np.int64]:

Xcsr = sparse.csr_matrix(X, dtype=dtype)

# csr_matrix will use int32 indices by default,

# up-casting those to int64 when necessary

if csr_index_dtype is np.int64:

Xcsr.indptr = Xcsr.indptr.astype(csr_index_dtype, copy=False)

Xcsr.indices = Xcsr.indices.astype(csr_index_dtype, copy=False)

assert Xcsr.indices.dtype == csr_index_dtype

assert Xcsr.indptr.dtype == csr_index_dtype

assert_array_almost_equal(sq_norm, row_norms(Xcsr, squared=True),

precision)

assert_array_almost_equal(np.sqrt(sq_norm), row_norms(Xcsr),

precision)

开发者ID:PacktPublishing，项目名称:Mastering-Elasticsearch-7.0，代码行数:29，

示例5: test_labels_assignment_and_inertia

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def test_labels_assignment_and_inertia():

# pure numpy implementation as easily auditable reference gold

# implementation

rng = np.random.RandomState(42)

noisy_centers = centers + rng.normal(size=centers.shape)

labels_gold = np.full(n_samples, -1, dtype=np.int)

mindist = np.empty(n_samples)

mindist.fill(np.infty)

for center_id in range(n_clusters):

dist = np.sum((X - noisy_centers[center_id]) ** 2, axis=1)

labels_gold[dist < mindist] = center_id

mindist = np.minimum(dist, mindist)

inertia_gold = mindist.sum()

assert (mindist >= 0.0).all()

assert (labels_gold != -1).all()

sample_weight = None

# perform label assignment using the dense array input

x_squared_norms = (X ** 2).sum(axis=1)

labels_array, inertia_array = _labels_inertia(

X, sample_weight, x_squared_norms, noisy_centers)

assert_array_almost_equal(inertia_array, inertia_gold)

assert_array_equal(labels_array, labels_gold)

# perform label assignment using the sparse CSR input

x_squared_norms_from_csr = row_norms(X_csr, squared=True)

labels_csr, inertia_csr = _labels_inertia(

X_csr, sample_weight, x_squared_norms_from_csr, noisy_centers)

assert_array_almost_equal(inertia_csr, inertia_gold)

assert_array_equal(labels_csr, labels_gold)

开发者ID:PacktPublishing，项目名称:Mastering-Elasticsearch-7.0，代码行数:33，

示例6: predict

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def predict(self, X, sample_weight=None):

"""Predict the closest cluster each sample in X belongs to.

In the vector quantization literature, `cluster_centers_` is called

the code book and each value returned by `predict` is the index of

the closest code in the code book.

Parameters

----------

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

New data to predict.

sample_weight : array-like, shape (n_samples,), optional

The weights for each observation in X. If None, all observations

are assigned equal weight (default: None)

Returns

-------

labels : array, shape [n_samples,]

Index of the cluster each sample belongs to.

"""

check_is_fitted(self)

X = self._check_test_data(X)

daal_ready = sample_weight is None and hasattr(X, '__array__') # or sp.isspmatrix_csr(X)

if daal_ready:

logging.info("sklearn.cluster.KMeans.predict: " + method_uses_daal)

return _daal4py_k_means_predict(X, self.n_clusters, self.cluster_centers_)[0]

else:

logging.info("sklearn.cluster.KMeans.predict: " + method_uses_sklearn)

x_squared_norms = row_norms(X, squared=True)

return _labels_inertia(X, sample_weight, x_squared_norms,

self.cluster_centers_)[0]

开发者ID:IntelPython，项目名称:daal4py，代码行数:37，

示例7: predict

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def predict(self, X, sample_weight=None):

"""Predict the closest cluster each sample in X belongs to.

In the vector quantization literature, `cluster_centers_` is called

the code book and each value returned by `predict` is the index of

the closest code in the code book.

Parameters

----------

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

New data to predict.

sample_weight : array-like, shape (n_samples,), optional

The weights for each observation in X. If None, all observations

are assigned equal weight (default: None)

Returns

-------

labels : array, shape [n_samples,]

Index of the cluster each sample belongs to.

"""

check_is_fitted(self, 'cluster_centers_')

X = self._check_test_data(X)

daal_ready = sample_weight is None and hasattr(X, '__array__') # or sp.isspmatrix_csr(X)

if daal_ready:

logging.info("sklearn.cluster.KMeans.predict: " + method_uses_daal)

return _daal4py_k_means_predict(X, self.n_clusters, self.cluster_centers_)[0]

else:

logging.info("sklearn.cluster.KMeans.predict: " + method_uses_sklearn)

x_squared_norms = row_norms(X, squared=True)

return _labels_inertia(X, sample_weight, x_squared_norms,

self.cluster_centers_)[0]

开发者ID:IntelPython，项目名称:daal4py，代码行数:37，

示例8: row_norms

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def row_norms(X, squared=False):

if isinstance(X, np.ndarray):

return skm.row_norms(X, squared=squared)

return X.map_blocks(

skm.row_norms, chunks=(X.chunks[0],), drop_axis=1, squared=squared

)

开发者ID:dask，项目名称:dask-ml，代码行数:8，

示例9: test_labels_assignment_and_inertia

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def test_labels_assignment_and_inertia():

# pure numpy implementation as easily auditable reference gold

# implementation

rng = np.random.RandomState(42)

noisy_centers = centers + rng.normal(size=centers.shape)

labels_gold = - np.ones(n_samples, dtype=np.int)

mindist = np.empty(n_samples)

mindist.fill(np.infty)

for center_id in range(n_clusters):

dist = np.sum((X - noisy_centers[center_id]) ** 2, axis=1)

labels_gold[dist < mindist] = center_id

mindist = np.minimum(dist, mindist)

inertia_gold = mindist.sum()

assert_true((mindist >= 0.0).all())

assert_true((labels_gold != -1).all())

# perform label assignment using the dense array input

x_squared_norms = (X ** 2).sum(axis=1)

labels_array, inertia_array = _labels_inertia(

X, x_squared_norms, noisy_centers)

assert_array_almost_equal(inertia_array, inertia_gold)

assert_array_equal(labels_array, labels_gold)

# perform label assignment using the sparse CSR input

x_squared_norms_from_csr = row_norms(X_csr, squared=True)

labels_csr, inertia_csr = _labels_inertia(

X_csr, x_squared_norms_from_csr, noisy_centers)

assert_array_almost_equal(inertia_csr, inertia_gold)

assert_array_equal(labels_csr, labels_gold)

开发者ID:alvarobartt，项目名称:twitter-stock-recommendation，代码行数:31，

示例10: test_get_auto_step_size

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def test_get_auto_step_size():

X = np.array([[1, 2, 3], [2, 3, 4], [2, 3, 2]], dtype=np.float64)

alpha = 1.2

fit_intercept = False

# sum the squares of the second sample because that's the largest

max_squared_sum = 4 + 9 + 16

max_squared_sum_ = row_norms(X, squared=True).max()

n_samples = X.shape[0]

assert_almost_equal(max_squared_sum, max_squared_sum_, decimal=4)

for saga in [True, False]:

for fit_intercept in (True, False):

if saga:

L_sqr = (max_squared_sum + alpha + int(fit_intercept))

L_log = (max_squared_sum + 4.0 * alpha +

int(fit_intercept)) / 4.0

mun_sqr = min(2 * n_samples * alpha, L_sqr)

mun_log = min(2 * n_samples * alpha, L_log)

step_size_sqr = 1 / (2 * L_sqr + mun_sqr)

step_size_log = 1 / (2 * L_log + mun_log)

else:

step_size_sqr = 1.0 / (max_squared_sum +

alpha + int(fit_intercept))

step_size_log = 4.0 / (max_squared_sum + 4.0 * alpha +

int(fit_intercept))

step_size_sqr_ = get_auto_step_size(max_squared_sum_, alpha,

"squared",

fit_intercept,

n_samples=n_samples,

is_saga=saga)

step_size_log_ = get_auto_step_size(max_squared_sum_, alpha, "log",

fit_intercept,

n_samples=n_samples,

is_saga=saga)

assert_almost_equal(step_size_sqr, step_size_sqr_, decimal=4)

assert_almost_equal(step_size_log, step_size_log_, decimal=4)

msg = 'Unknown loss function for SAG solver, got wrong instead of'

assert_raise_message(ValueError, msg, get_auto_step_size,

max_squared_sum_, alpha, "wrong", fit_intercept)

开发者ID:PacktPublishing，项目名称:Mastering-Elasticsearch-7.0，代码行数:44，

示例11: _daal4py_check

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def _daal4py_check(self, X, y, check_input):

_fptype = getFPType(X)

#check alpha

if self.alpha == 0:

warnings.warn("With alpha=0, this algorithm does not converge "

"well. You are advised to use the LinearRegression "

"estimator", stacklevel=2)

#check precompute

if isinstance(self.precompute, np.ndarray):

if check_input:

check_array(self.precompute, dtype=_fptype)

self.precompute = make2d(self.precompute)

#only for compliance with Sklearn

if self.fit_intercept:

X_offset = np.average(X, axis=0, weights=None)

if self.normalize:

X_scale = row_norms(X)

if np.isscalar(X_scale):

if X_scale == .0:

X_scale = 1.

elif isinstance(X_scale, np.ndarray):

X_scale[X_scale == 0.0] = 1.0

else:

X_scale = np.ones(X.shape[1], dtype=_fptype)

else:

X_offset = np.zeros(X.shape[1], dtype=_fptype)

X_scale = np.ones(X.shape[1], dtype=_fptype)

if (self.fit_intercept and not np.allclose(X_offset, np.zeros(X.shape[1])) or

self.normalize and not np.allclose(X_scale, np.ones(X.shape[1]))):

warnings.warn("Gram matrix was provided but X was centered"

" to fit intercept, "

"or X was normalized : recomputing Gram matrix.",

UserWarning)

else:

if self.precompute not in [False, True, 'auto']:

raise ValueError("precompute should be one of True, False, "

"'auto' or array-like. Got %r" % self.precompute)

#check selection

if self.selection not in ['random', 'cyclic']:

raise ValueError("selection should be either random or cyclic.")

开发者ID:IntelPython，项目名称:daal4py，代码行数:45，

示例12: fit

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def fit(self, neigh_dist: np.ndarray, neigh_ind: np.ndarray, X: np.ndarray,

assume_sorted: bool = True, *args, **kwargs) -> DisSimLocal:

""" Fit the model using X, neigh_dist, and neigh_ind as training data.

Parameters

----------

neigh_dist: np.ndarray, shape (n_samples, n_neighbors)

Distance matrix of training objects (rows) against their

individual k nearest neighbors (colums).

neigh_ind: np.ndarray, shape (n_samples, n_neighbors)

Neighbor indices corresponding to the values in neigh_dist.

X: np.ndarray, shape (n_samples, n_features)

Training data, where n_samples is the number of vectors,

and n_features their dimensionality (number of features).

assume_sorted: bool, default = True

Assume input matrices are sorted according to neigh_dist.

If False, these are sorted here.

"""

# Check equal number of rows and columns

check_consistent_length(neigh_ind, neigh_dist)

check_consistent_length(neigh_ind.T, neigh_dist.T)

X = check_array(X)

try:

if self.k <= 0:

raise ValueError(f"Expected k > 0. Got {self.k}")

except TypeError:

raise TypeError(f'Expected k: int > 0. Got {self.k}')

k = self.k

if k > neigh_ind.shape[1]:

warnings.warn(f'Neighborhood parameter k larger than provided neighbors in neigh_dist, neigh_ind. '

f'Will reduce to k={neigh_ind.shape[1]}.')

k = neigh_ind.shape[1]

# Calculate local neighborhood centroids among the training points

if assume_sorted:

knn = neigh_ind[:, :k]

else:

mask = np.argpartition(neigh_dist, kth=k-1)[:, :k]

knn = np.take_along_axis(neigh_ind, mask, axis=1)

centroids = X[knn].mean(axis=1)

dist_to_cent = row_norms(X - centroids, squared=True)

self.X_train_ = X

self.X_train_centroids_ = centroids

self.X_train_dist_to_centroids_ = dist_to_cent

return self

开发者ID:VarIr，项目名称:scikit-hubness，代码行数:53，

示例13: fit

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def fit(self, X, y):

"""Fit factorization machine to training data.

Parameters

----------

X : array-like or sparse, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples

and n_features is the number of features.

y : array-like, shape = [n_samples]

Target values.

Returns

-------

self : Estimator

Returns self.

"""

if self.degree > 3:

raise ValueError("FMs with degree >3 not yet supported.")

X, y = self._check_X_y(X, y)

X = self._augment(X)

n_features = X.shape[1] # augmented

X_col_norms = row_norms(X.T, squared=True)

dataset = get_dataset(X, order="fortran")

rng = check_random_state(self.random_state)

loss_obj = self._get_loss(self.loss)

if not (self.warm_start and hasattr(self, 'w_')):

self.w_ = np.zeros(n_features, dtype=np.double)

if self.fit_lower == 'explicit':

n_orders = self.degree - 1

else:

n_orders = 1

if not (self.warm_start and hasattr(self, 'P_')):

self.P_ = 0.01 * rng.randn(n_orders, self.n_components, n_features)

if not (self.warm_start and hasattr(self, 'lams_')):

if self.init_lambdas == 'ones':

self.lams_ = np.ones(self.n_components)

elif self.init_lambdas == 'random_signs':

self.lams_ = np.sign(rng.randn(self.n_components))

else:

raise ValueError("Lambdas must be initialized as ones "

"(init_lambdas='ones') or as random "

"+/- 1 (init_lambdas='random_signs').")

y_pred = self._get_output(X)

converged, self.n_iter_ = _cd_direct_ho(

self.P_, self.w_, dataset, X_col_norms, y, y_pred,

self.lams_, self.degree, self.alpha, self.beta, self.fit_linear,

self.fit_lower == 'explicit', loss_obj, self.max_iter,

self.tol, self.verbose)

if not converged:

warnings.warn("Objective did not converge. Increase max_iter.")

return self

开发者ID:scikit-learn-contrib，项目名称:polylearn，代码行数:62，

示例14: test_sub_kmeans

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# 需要导入模块: from sklearn.utils import extmath [as 别名]

# 或者: from sklearn.utils.extmath import row_norms [as 别名]

def test_sub_kmeans(self):

def conv(scope, operator, container):

X = operator.inputs[0]

out = operator.outputs

op = operator.raw_operator

C = op.cluster_centers_

C2 = row_norms(C, squared=True).astype(container.dtype)

C = C.astype(container.dtype)

rs = OnnxReduceSumSquare(

X, axes=[1], keepdims=1,

op_version=container.target_opset)

N = X.type.shape[0]

if isinstance(N, int):

zeros = np.zeros((N, ))

else:

zeros = OnnxMul(

rs, np.array([0], dtype=np.float32),

op_version=container.target_opset)

z = OnnxAdd(

rs,

OnnxGemm(

X, C, zeros, alpha=-2., transB=1,

op_version=container.target_opset),

op_version=container.target_opset)

y2 = OnnxAdd(C2, z, op_version=container.target_opset)

lo = OnnxArgMin(

y2, axis=1, keepdims=0, output_names=out[:1],

op_version=container.target_opset)

y2s = OnnxSqrt(

y2, output_names=out[1:],

op_version=container.target_opset)

lo.add_to(scope, container)

y2s.add_to(scope, container)

data = load_iris()

X = data.data

model = KMeans(n_clusters=3)

model.fit(X)

model_onnx = convert_sklearn(

model, 'a-kmeans',

[('input', FloatTensorType([None, X.shape[1]]))],

custom_conversion_functions={KMeans: conv},

target_opset=TARGET_OPSET)

dump_data_and_model(X.astype(np.float32)[40:60], model, model_onnx,

basename="SklearnKMeansCustom-Dec4")

开发者ID:onnx，项目名称:sklearn-onnx，代码行数:54，

注：本文中的sklearn.utils.extmath.row_norms方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。