doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
|---|---|
transform(X) [source]
Encode the data as a sparse combination of the dictionary atoms. Coding method is determined by the object parameter transform_algorithm. Parameters
Xndarray of shape (n_samples, n_features)
Test data to be transformed, must have the same number of features as the data used to train the mo... | sklearn.modules.generated.sklearn.decomposition.dictionarylearning#sklearn.decomposition.DictionaryLearning.transform |
sklearn.decomposition.dict_learning(X, n_components, *, alpha, max_iter=100, tol=1e-08, method='lars', n_jobs=None, dict_init=None, code_init=None, callback=None, verbose=False, random_state=None, return_n_iter=False, positive_dict=False, positive_code=False, method_max_iter=1000) [source]
Solves a dictionary learnin... | sklearn.modules.generated.sklearn.decomposition.dict_learning#sklearn.decomposition.dict_learning |
sklearn.decomposition.dict_learning_online(X, n_components=2, *, alpha=1, n_iter=100, return_code=True, dict_init=None, callback=None, batch_size=3, verbose=False, shuffle=True, n_jobs=None, method='lars', iter_offset=0, random_state=None, return_inner_stats=False, inner_stats=None, return_n_iter=False, positive_dict=F... | sklearn.modules.generated.sklearn.decomposition.dict_learning_online#sklearn.decomposition.dict_learning_online |
class sklearn.decomposition.FactorAnalysis(n_components=None, *, tol=0.01, copy=True, max_iter=1000, noise_variance_init=None, svd_method='randomized', iterated_power=3, rotation=None, random_state=0) [source]
Factor Analysis (FA). A simple linear generative model with Gaussian latent variables. The observations are ... | sklearn.modules.generated.sklearn.decomposition.factoranalysis#sklearn.decomposition.FactorAnalysis |
sklearn.decomposition.FactorAnalysis
class sklearn.decomposition.FactorAnalysis(n_components=None, *, tol=0.01, copy=True, max_iter=1000, noise_variance_init=None, svd_method='randomized', iterated_power=3, rotation=None, random_state=0) [source]
Factor Analysis (FA). A simple linear generative model with Gaussian ... | sklearn.modules.generated.sklearn.decomposition.factoranalysis |
fit(X, y=None) [source]
Fit the FactorAnalysis model to X using SVD based approach Parameters
Xarray-like of shape (n_samples, n_features)
Training data.
yIgnored
Returns
self | sklearn.modules.generated.sklearn.decomposition.factoranalysis#sklearn.decomposition.FactorAnalysis.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.decomposition.factoranalysis#sklearn.decomposition.FactorAnalysis.fit_transform |
get_covariance() [source]
Compute data covariance with the FactorAnalysis model. cov = components_.T * components_ + diag(noise_variance) Returns
covndarray of shape (n_features, n_features)
Estimated covariance of data. | sklearn.modules.generated.sklearn.decomposition.factoranalysis#sklearn.decomposition.FactorAnalysis.get_covariance |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.decomposition.factoranalysis#sklearn.decomposition.FactorAnalysis.get_params |
get_precision() [source]
Compute data precision matrix with the FactorAnalysis model. Returns
precisionndarray of shape (n_features, n_features)
Estimated precision of data. | sklearn.modules.generated.sklearn.decomposition.factoranalysis#sklearn.decomposition.FactorAnalysis.get_precision |
score(X, y=None) [source]
Compute the average log-likelihood of the samples Parameters
Xndarray of shape (n_samples, n_features)
The data
yIgnored
Returns
llfloat
Average log-likelihood of the samples under the current model | sklearn.modules.generated.sklearn.decomposition.factoranalysis#sklearn.decomposition.FactorAnalysis.score |
score_samples(X) [source]
Compute the log-likelihood of each sample Parameters
Xndarray of shape (n_samples, n_features)
The data Returns
llndarray of shape (n_samples,)
Log-likelihood of each sample under the current model | sklearn.modules.generated.sklearn.decomposition.factoranalysis#sklearn.decomposition.FactorAnalysis.score_samples |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.decomposition.factoranalysis#sklearn.decomposition.FactorAnalysis.set_params |
transform(X) [source]
Apply dimensionality reduction to X using the model. Compute the expected mean of the latent variables. See Barber, 21.2.33 (or Bishop, 12.66). Parameters
Xarray-like of shape (n_samples, n_features)
Training data. Returns
X_newndarray of shape (n_samples, n_components)
The latent ... | sklearn.modules.generated.sklearn.decomposition.factoranalysis#sklearn.decomposition.FactorAnalysis.transform |
class sklearn.decomposition.FastICA(n_components=None, *, algorithm='parallel', whiten=True, fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, random_state=None) [source]
FastICA: a fast algorithm for Independent Component Analysis. Read more in the User Guide. Parameters
n_componentsint, defau... | sklearn.modules.generated.sklearn.decomposition.fastica#sklearn.decomposition.FastICA |
sklearn.decomposition.fastica(X, n_components=None, *, algorithm='parallel', whiten=True, fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, random_state=None, return_X_mean=False, compute_sources=True, return_n_iter=False) [source]
Perform Fast Independent Component Analysis. Read more in the User ... | sklearn.modules.generated.fastica-function#sklearn.decomposition.fastica |
sklearn.decomposition.FastICA
class sklearn.decomposition.FastICA(n_components=None, *, algorithm='parallel', whiten=True, fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, random_state=None) [source]
FastICA: a fast algorithm for Independent Component Analysis. Read more in the User Guide. Para... | sklearn.modules.generated.sklearn.decomposition.fastica |
fit(X, y=None) [source]
Fit the model to X. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yIgnored
Returns
self | sklearn.modules.generated.sklearn.decomposition.fastica#sklearn.decomposition.FastICA.fit |
fit_transform(X, y=None) [source]
Fit the model and recover the sources from X. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yIgnored
Returns
X_newndarray of shape (n_samples, n_components) | sklearn.modules.generated.sklearn.decomposition.fastica#sklearn.decomposition.FastICA.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.decomposition.fastica#sklearn.decomposition.FastICA.get_params |
inverse_transform(X, copy=True) [source]
Transform the sources back to the mixed data (apply mixing matrix). Parameters
Xarray-like of shape (n_samples, n_components)
Sources, where n_samples is the number of samples and n_components is the number of components.
copybool, default=True
If False, data passed ... | sklearn.modules.generated.sklearn.decomposition.fastica#sklearn.decomposition.FastICA.inverse_transform |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.decomposition.fastica#sklearn.decomposition.FastICA.set_params |
transform(X, copy=True) [source]
Recover the sources from X (apply the unmixing matrix). Parameters
Xarray-like of shape (n_samples, n_features)
Data to transform, where n_samples is the number of samples and n_features is the number of features.
copybool, default=True
If False, data passed to fit can be ov... | sklearn.modules.generated.sklearn.decomposition.fastica#sklearn.decomposition.FastICA.transform |
class sklearn.decomposition.IncrementalPCA(n_components=None, *, whiten=False, copy=True, batch_size=None) [source]
Incremental principal components analysis (IPCA). Linear dimensionality reduction using Singular Value Decomposition of the data, keeping only the most significant singular vectors to project the data t... | sklearn.modules.generated.sklearn.decomposition.incrementalpca#sklearn.decomposition.IncrementalPCA |
sklearn.decomposition.IncrementalPCA
class sklearn.decomposition.IncrementalPCA(n_components=None, *, whiten=False, copy=True, batch_size=None) [source]
Incremental principal components analysis (IPCA). Linear dimensionality reduction using Singular Value Decomposition of the data, keeping only the most significant... | sklearn.modules.generated.sklearn.decomposition.incrementalpca |
fit(X, y=None) [source]
Fit the model with X, using minibatches of size batch_size. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yIgnored
Returns
selfobject
Returns the instanc... | sklearn.modules.generated.sklearn.decomposition.incrementalpca#sklearn.decomposition.IncrementalPCA.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.decomposition.incrementalpca#sklearn.decomposition.IncrementalPCA.fit_transform |
get_covariance() [source]
Compute data covariance with the generative model. cov = components_.T * S**2 * components_ + sigma2 * eye(n_features) where S**2 contains the explained variances, and sigma2 contains the noise variances. Returns
covarray, shape=(n_features, n_features)
Estimated covariance of data. | sklearn.modules.generated.sklearn.decomposition.incrementalpca#sklearn.decomposition.IncrementalPCA.get_covariance |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.decomposition.incrementalpca#sklearn.decomposition.IncrementalPCA.get_params |
get_precision() [source]
Compute data precision matrix with the generative model. Equals the inverse of the covariance but computed with the matrix inversion lemma for efficiency. Returns
precisionarray, shape=(n_features, n_features)
Estimated precision of data. | sklearn.modules.generated.sklearn.decomposition.incrementalpca#sklearn.decomposition.IncrementalPCA.get_precision |
inverse_transform(X) [source]
Transform data back to its original space. In other words, return an input X_original whose transform would be X. Parameters
Xarray-like, shape (n_samples, n_components)
New data, where n_samples is the number of samples and n_components is the number of components. Returns
X_... | sklearn.modules.generated.sklearn.decomposition.incrementalpca#sklearn.decomposition.IncrementalPCA.inverse_transform |
partial_fit(X, y=None, check_input=True) [source]
Incremental fit with X. All of X is processed as a single batch. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
check_inputbool, default=True
Run check... | sklearn.modules.generated.sklearn.decomposition.incrementalpca#sklearn.decomposition.IncrementalPCA.partial_fit |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.decomposition.incrementalpca#sklearn.decomposition.IncrementalPCA.set_params |
transform(X) [source]
Apply dimensionality reduction to X. X is projected on the first principal components previously extracted from a training set, using minibatches of size batch_size if X is sparse. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
New data, where n_samples is the num... | sklearn.modules.generated.sklearn.decomposition.incrementalpca#sklearn.decomposition.IncrementalPCA.transform |
class sklearn.decomposition.KernelPCA(n_components=None, *, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None, alpha=1.0, fit_inverse_transform=False, eigen_solver='auto', tol=0, max_iter=None, remove_zero_eig=False, random_state=None, copy_X=True, n_jobs=None) [source]
Kernel Principal component ana... | sklearn.modules.generated.sklearn.decomposition.kernelpca#sklearn.decomposition.KernelPCA |
sklearn.decomposition.KernelPCA
class sklearn.decomposition.KernelPCA(n_components=None, *, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None, alpha=1.0, fit_inverse_transform=False, eigen_solver='auto', tol=0, max_iter=None, remove_zero_eig=False, random_state=None, copy_X=True, n_jobs=None) [source... | sklearn.modules.generated.sklearn.decomposition.kernelpca |
fit(X, y=None) [source]
Fit the model from data in X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples in the number of samples and n_features is the number of features. Returns
selfobject
Returns the instance itself. | sklearn.modules.generated.sklearn.decomposition.kernelpca#sklearn.decomposition.KernelPCA.fit |
fit_transform(X, y=None, **params) [source]
Fit the model from data in X and transform X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples in the number of samples and n_features is the number of features. Returns
X_newndarray of shape (n_samples, n... | sklearn.modules.generated.sklearn.decomposition.kernelpca#sklearn.decomposition.KernelPCA.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.decomposition.kernelpca#sklearn.decomposition.KernelPCA.get_params |
inverse_transform(X) [source]
Transform X back to original space. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_components)
Returns
X_newndarray of shape (n_samples, n_features)
References “Learning to Find Pre-Images”, G BakIr et al, 2004. | sklearn.modules.generated.sklearn.decomposition.kernelpca#sklearn.decomposition.KernelPCA.inverse_transform |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.decomposition.kernelpca#sklearn.decomposition.KernelPCA.set_params |
transform(X) [source]
Transform X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Returns
X_newndarray of shape (n_samples, n_components) | sklearn.modules.generated.sklearn.decomposition.kernelpca#sklearn.decomposition.KernelPCA.transform |
class sklearn.decomposition.LatentDirichletAllocation(n_components=10, *, doc_topic_prior=None, topic_word_prior=None, learning_method='batch', learning_decay=0.7, learning_offset=10.0, max_iter=10, batch_size=128, evaluate_every=- 1, total_samples=1000000.0, perp_tol=0.1, mean_change_tol=0.001, max_doc_update_iter=100... | sklearn.modules.generated.sklearn.decomposition.latentdirichletallocation#sklearn.decomposition.LatentDirichletAllocation |
sklearn.decomposition.LatentDirichletAllocation
class sklearn.decomposition.LatentDirichletAllocation(n_components=10, *, doc_topic_prior=None, topic_word_prior=None, learning_method='batch', learning_decay=0.7, learning_offset=10.0, max_iter=10, batch_size=128, evaluate_every=- 1, total_samples=1000000.0, perp_tol=0... | sklearn.modules.generated.sklearn.decomposition.latentdirichletallocation |
fit(X, y=None) [source]
Learn model for the data X with variational Bayes method. When learning_method is ‘online’, use mini-batch update. Otherwise, use batch update. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Document word matrix.
yIgnored
Returns
self | sklearn.modules.generated.sklearn.decomposition.latentdirichletallocation#sklearn.decomposition.LatentDirichletAllocation.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.decomposition.latentdirichletallocation#sklearn.decomposition.LatentDirichletAllocation.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.decomposition.latentdirichletallocation#sklearn.decomposition.LatentDirichletAllocation.get_params |
partial_fit(X, y=None) [source]
Online VB with Mini-Batch update. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Document word matrix.
yIgnored
Returns
self | sklearn.modules.generated.sklearn.decomposition.latentdirichletallocation#sklearn.decomposition.LatentDirichletAllocation.partial_fit |
perplexity(X, sub_sampling=False) [source]
Calculate approximate perplexity for data X. Perplexity is defined as exp(-1. * log-likelihood per word) Changed in version 0.19: doc_topic_distr argument has been deprecated and is ignored because user no longer has access to unnormalized distribution Parameters
X{arr... | sklearn.modules.generated.sklearn.decomposition.latentdirichletallocation#sklearn.decomposition.LatentDirichletAllocation.perplexity |
score(X, y=None) [source]
Calculate approximate log-likelihood as score. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Document word matrix.
yIgnored
Returns
scorefloat
Use approximate bound as score. | sklearn.modules.generated.sklearn.decomposition.latentdirichletallocation#sklearn.decomposition.LatentDirichletAllocation.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.decomposition.latentdirichletallocation#sklearn.decomposition.LatentDirichletAllocation.set_params |
transform(X) [source]
Transform data X according to the fitted model. Changed in version 0.18: doc_topic_distr is now normalized Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Document word matrix. Returns
doc_topic_distrndarray of shape (n_samples, n_components)
Document top... | sklearn.modules.generated.sklearn.decomposition.latentdirichletallocation#sklearn.decomposition.LatentDirichletAllocation.transform |
class sklearn.decomposition.MiniBatchDictionaryLearning(n_components=None, *, alpha=1, n_iter=1000, fit_algorithm='lars', n_jobs=None, batch_size=3, shuffle=True, dict_init=None, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, verbose=False, split_sign=False, random_state=None, positive... | sklearn.modules.generated.sklearn.decomposition.minibatchdictionarylearning#sklearn.decomposition.MiniBatchDictionaryLearning |
sklearn.decomposition.MiniBatchDictionaryLearning
class sklearn.decomposition.MiniBatchDictionaryLearning(n_components=None, *, alpha=1, n_iter=1000, fit_algorithm='lars', n_jobs=None, batch_size=3, shuffle=True, dict_init=None, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, verbose=... | sklearn.modules.generated.sklearn.decomposition.minibatchdictionarylearning |
fit(X, y=None) [source]
Fit the model from data in X. Parameters
Xarray-like of shape (n_samples, n_features)
Training vector, where n_samples in the number of samples and n_features is the number of features.
yIgnored
Returns
selfobject
Returns the instance itself. | sklearn.modules.generated.sklearn.decomposition.minibatchdictionarylearning#sklearn.decomposition.MiniBatchDictionaryLearning.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.decomposition.minibatchdictionarylearning#sklearn.decomposition.MiniBatchDictionaryLearning.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.decomposition.minibatchdictionarylearning#sklearn.decomposition.MiniBatchDictionaryLearning.get_params |
partial_fit(X, y=None, iter_offset=None) [source]
Updates the model using the data in X as a mini-batch. Parameters
Xarray-like of shape (n_samples, n_features)
Training vector, where n_samples in the number of samples and n_features is the number of features.
yIgnored
iter_offsetint, default=None
The num... | sklearn.modules.generated.sklearn.decomposition.minibatchdictionarylearning#sklearn.decomposition.MiniBatchDictionaryLearning.partial_fit |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.decomposition.minibatchdictionarylearning#sklearn.decomposition.MiniBatchDictionaryLearning.set_params |
transform(X) [source]
Encode the data as a sparse combination of the dictionary atoms. Coding method is determined by the object parameter transform_algorithm. Parameters
Xndarray of shape (n_samples, n_features)
Test data to be transformed, must have the same number of features as the data used to train the mo... | sklearn.modules.generated.sklearn.decomposition.minibatchdictionarylearning#sklearn.decomposition.MiniBatchDictionaryLearning.transform |
class sklearn.decomposition.MiniBatchSparsePCA(n_components=None, *, alpha=1, ridge_alpha=0.01, n_iter=100, callback=None, batch_size=3, verbose=False, shuffle=True, n_jobs=None, method='lars', random_state=None) [source]
Mini-batch Sparse Principal Components Analysis Finds the set of sparse components that can opti... | sklearn.modules.generated.sklearn.decomposition.minibatchsparsepca#sklearn.decomposition.MiniBatchSparsePCA |
sklearn.decomposition.MiniBatchSparsePCA
class sklearn.decomposition.MiniBatchSparsePCA(n_components=None, *, alpha=1, ridge_alpha=0.01, n_iter=100, callback=None, batch_size=3, verbose=False, shuffle=True, n_jobs=None, method='lars', random_state=None) [source]
Mini-batch Sparse Principal Components Analysis Finds... | sklearn.modules.generated.sklearn.decomposition.minibatchsparsepca |
fit(X, y=None) [source]
Fit the model from data in X. Parameters
Xarray-like of shape (n_samples, n_features)
Training vector, where n_samples in the number of samples and n_features is the number of features.
yIgnored
Returns
selfobject
Returns the instance itself. | sklearn.modules.generated.sklearn.decomposition.minibatchsparsepca#sklearn.decomposition.MiniBatchSparsePCA.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.decomposition.minibatchsparsepca#sklearn.decomposition.MiniBatchSparsePCA.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.decomposition.minibatchsparsepca#sklearn.decomposition.MiniBatchSparsePCA.get_params |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.decomposition.minibatchsparsepca#sklearn.decomposition.MiniBatchSparsePCA.set_params |
transform(X) [source]
Least Squares projection of the data onto the sparse components. To avoid instability issues in case the system is under-determined, regularization can be applied (Ridge regression) via the ridge_alpha parameter. Note that Sparse PCA components orthogonality is not enforced as in PCA hence one c... | sklearn.modules.generated.sklearn.decomposition.minibatchsparsepca#sklearn.decomposition.MiniBatchSparsePCA.transform |
class sklearn.decomposition.NMF(n_components=None, *, init='warn', solver='cd', beta_loss='frobenius', tol=0.0001, max_iter=200, random_state=None, alpha=0.0, l1_ratio=0.0, verbose=0, shuffle=False, regularization='both') [source]
Non-Negative Matrix Factorization (NMF). Find two non-negative matrices (W, H) whose pr... | sklearn.modules.generated.sklearn.decomposition.nmf#sklearn.decomposition.NMF |
sklearn.decomposition.NMF
class sklearn.decomposition.NMF(n_components=None, *, init='warn', solver='cd', beta_loss='frobenius', tol=0.0001, max_iter=200, random_state=None, alpha=0.0, l1_ratio=0.0, verbose=0, shuffle=False, regularization='both') [source]
Non-Negative Matrix Factorization (NMF). Find two non-negat... | sklearn.modules.generated.sklearn.decomposition.nmf |
fit(X, y=None, **params) [source]
Learn a NMF model for the data X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Data matrix to be decomposed
yIgnored
Returns
self | sklearn.modules.generated.sklearn.decomposition.nmf#sklearn.decomposition.NMF.fit |
fit_transform(X, y=None, W=None, H=None) [source]
Learn a NMF model for the data X and returns the transformed data. This is more efficient than calling fit followed by transform. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Data matrix to be decomposed
yIgnored
Warray-like of sh... | sklearn.modules.generated.sklearn.decomposition.nmf#sklearn.decomposition.NMF.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.decomposition.nmf#sklearn.decomposition.NMF.get_params |
inverse_transform(W) [source]
Transform data back to its original space. Parameters
W{ndarray, sparse matrix} of shape (n_samples, n_components)
Transformed data matrix. Returns
X{ndarray, sparse matrix} of shape (n_samples, n_features)
Data matrix of original shape. New in version 0.18: .. | sklearn.modules.generated.sklearn.decomposition.nmf#sklearn.decomposition.NMF.inverse_transform |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.decomposition.nmf#sklearn.decomposition.NMF.set_params |
transform(X) [source]
Transform the data X according to the fitted NMF model. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Data matrix to be transformed by the model. Returns
Wndarray of shape (n_samples, n_components)
Transformed data. | sklearn.modules.generated.sklearn.decomposition.nmf#sklearn.decomposition.NMF.transform |
sklearn.decomposition.non_negative_factorization(X, W=None, H=None, n_components=None, *, init='warn', update_H=True, solver='cd', beta_loss='frobenius', tol=0.0001, max_iter=200, alpha=0.0, l1_ratio=0.0, regularization=None, random_state=None, verbose=0, shuffle=False) [source]
Compute Non-negative Matrix Factorizat... | sklearn.modules.generated.sklearn.decomposition.non_negative_factorization#sklearn.decomposition.non_negative_factorization |
class sklearn.decomposition.PCA(n_components=None, *, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None) [source]
Principal component analysis (PCA). Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space. T... | sklearn.modules.generated.sklearn.decomposition.pca#sklearn.decomposition.PCA |
sklearn.decomposition.PCA
class sklearn.decomposition.PCA(n_components=None, *, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None) [source]
Principal component analysis (PCA). Linear dimensionality reduction using Singular Value Decomposition of the data to project it to ... | sklearn.modules.generated.sklearn.decomposition.pca |
fit(X, y=None) [source]
Fit the model with X. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yIgnored
Returns
selfobject
Returns the instance itself. | sklearn.modules.generated.sklearn.decomposition.pca#sklearn.decomposition.PCA.fit |
fit_transform(X, y=None) [source]
Fit the model with X and apply the dimensionality reduction on X. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yIgnored
Returns
X_newndarray of shape (n_samples,... | sklearn.modules.generated.sklearn.decomposition.pca#sklearn.decomposition.PCA.fit_transform |
get_covariance() [source]
Compute data covariance with the generative model. cov = components_.T * S**2 * components_ + sigma2 * eye(n_features) where S**2 contains the explained variances, and sigma2 contains the noise variances. Returns
covarray, shape=(n_features, n_features)
Estimated covariance of data. | sklearn.modules.generated.sklearn.decomposition.pca#sklearn.decomposition.PCA.get_covariance |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.decomposition.pca#sklearn.decomposition.PCA.get_params |
get_precision() [source]
Compute data precision matrix with the generative model. Equals the inverse of the covariance but computed with the matrix inversion lemma for efficiency. Returns
precisionarray, shape=(n_features, n_features)
Estimated precision of data. | sklearn.modules.generated.sklearn.decomposition.pca#sklearn.decomposition.PCA.get_precision |
inverse_transform(X) [source]
Transform data back to its original space. In other words, return an input X_original whose transform would be X. Parameters
Xarray-like, shape (n_samples, n_components)
New data, where n_samples is the number of samples and n_components is the number of components. Returns
X_... | sklearn.modules.generated.sklearn.decomposition.pca#sklearn.decomposition.PCA.inverse_transform |
score(X, y=None) [source]
Return the average log-likelihood of all samples. See. “Pattern Recognition and Machine Learning” by C. Bishop, 12.2.1 p. 574 or http://www.miketipping.com/papers/met-mppca.pdf Parameters
Xarray-like of shape (n_samples, n_features)
The data.
yIgnored
Returns
llfloat
Average ... | sklearn.modules.generated.sklearn.decomposition.pca#sklearn.decomposition.PCA.score |
score_samples(X) [source]
Return the log-likelihood of each sample. See. “Pattern Recognition and Machine Learning” by C. Bishop, 12.2.1 p. 574 or http://www.miketipping.com/papers/met-mppca.pdf Parameters
Xarray-like of shape (n_samples, n_features)
The data. Returns
llndarray of shape (n_samples,)
Log... | sklearn.modules.generated.sklearn.decomposition.pca#sklearn.decomposition.PCA.score_samples |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.decomposition.pca#sklearn.decomposition.PCA.set_params |
transform(X) [source]
Apply dimensionality reduction to X. X is projected on the first principal components previously extracted from a training set. Parameters
Xarray-like, shape (n_samples, n_features)
New data, where n_samples is the number of samples and n_features is the number of features. Returns
X... | sklearn.modules.generated.sklearn.decomposition.pca#sklearn.decomposition.PCA.transform |
class sklearn.decomposition.SparseCoder(dictionary, *, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, split_sign=False, n_jobs=None, positive_code=False, transform_max_iter=1000) [source]
Sparse coding Finds a sparse representation of data against a fixed, precomputed dictionary. Eac... | sklearn.modules.generated.sklearn.decomposition.sparsecoder#sklearn.decomposition.SparseCoder |
sklearn.decomposition.SparseCoder
class sklearn.decomposition.SparseCoder(dictionary, *, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, split_sign=False, n_jobs=None, positive_code=False, transform_max_iter=1000) [source]
Sparse coding Finds a sparse representation of data against ... | sklearn.modules.generated.sklearn.decomposition.sparsecoder |
fit(X, y=None) [source]
Do nothing and return the estimator unchanged. This method is just there to implement the usual API and hence work in pipelines. Parameters
XIgnored
yIgnored
Returns
selfobject | sklearn.modules.generated.sklearn.decomposition.sparsecoder#sklearn.decomposition.SparseCoder.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.decomposition.sparsecoder#sklearn.decomposition.SparseCoder.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.decomposition.sparsecoder#sklearn.decomposition.SparseCoder.get_params |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.decomposition.sparsecoder#sklearn.decomposition.SparseCoder.set_params |
transform(X, y=None) [source]
Encode the data as a sparse combination of the dictionary atoms. Coding method is determined by the object parameter transform_algorithm. Parameters
Xndarray of shape (n_samples, n_features)
Test data to be transformed, must have the same number of features as the data used to trai... | sklearn.modules.generated.sklearn.decomposition.sparsecoder#sklearn.decomposition.SparseCoder.transform |
class sklearn.decomposition.SparsePCA(n_components=None, *, alpha=1, ridge_alpha=0.01, max_iter=1000, tol=1e-08, method='lars', n_jobs=None, U_init=None, V_init=None, verbose=False, random_state=None) [source]
Sparse Principal Components Analysis (SparsePCA). Finds the set of sparse components that can optimally reco... | sklearn.modules.generated.sklearn.decomposition.sparsepca#sklearn.decomposition.SparsePCA |
sklearn.decomposition.SparsePCA
class sklearn.decomposition.SparsePCA(n_components=None, *, alpha=1, ridge_alpha=0.01, max_iter=1000, tol=1e-08, method='lars', n_jobs=None, U_init=None, V_init=None, verbose=False, random_state=None) [source]
Sparse Principal Components Analysis (SparsePCA). Finds the set of sparse ... | sklearn.modules.generated.sklearn.decomposition.sparsepca |
fit(X, y=None) [source]
Fit the model from data in X. Parameters
Xarray-like of shape (n_samples, n_features)
Training vector, where n_samples in the number of samples and n_features is the number of features.
yIgnored
Returns
selfobject
Returns the instance itself. | sklearn.modules.generated.sklearn.decomposition.sparsepca#sklearn.decomposition.SparsePCA.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.decomposition.sparsepca#sklearn.decomposition.SparsePCA.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.decomposition.sparsepca#sklearn.decomposition.SparsePCA.get_params |
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