diff --git a/daal4py/_sources/algorithms.rst.txt b/daal4py/_sources/algorithms.rst.txt index 3d23cd2..17f2ef0 100755 --- a/daal4py/_sources/algorithms.rst.txt +++ b/daal4py/_sources/algorithms.rst.txt @@ -13,7 +13,7 @@ Parameters and semantics are described in |onedal-dg-classification-decision-for .. rubric:: Examples: - `Single-Process Decision Forest Classification - `__ + `__ .. autoclass:: daal4py.decision_forest_classification_training :members: compute @@ -33,7 +33,7 @@ Parameters and semantics are described in |onedal-dg-classification-decision-tre .. rubric:: Examples: - `Single-Process Decision Tree Classification - `__ + `__ .. autoclass:: daal4py.decision_tree_classification_training :members: compute @@ -53,7 +53,7 @@ Parameters and semantics are described in |onedal-dg-classification-gradient-boo .. rubric:: Examples: - `Single-Process Gradient Boosted Classification - `__ + `__ .. autoclass:: daal4py.gbt_classification_training :members: compute @@ -73,7 +73,7 @@ Parameters and semantics are described in |onedal-dg-k-nearest-neighbors-knn|_. .. rubric:: Examples: - `Single-Process kNN - `__ + `__ .. autoclass:: daal4py.kdtree_knn_classification_training :members: compute @@ -108,7 +108,7 @@ Parameters and semantics are described in |onedal-dg-classification-adaboost|_. .. rubric:: Examples: - `Single-Process AdaBoost Classification - `__ + `__ .. autoclass:: daal4py.adaboost_training :members: compute @@ -128,7 +128,7 @@ Parameters and semantics are described in |onedal-dg-classification-brownboost|_ .. rubric:: Examples: - `Single-Process BrownBoost Classification - `__ + `__ .. autoclass:: daal4py.brownboost_training :members: compute @@ -148,7 +148,7 @@ Parameters and semantics are described in |onedal-dg-classification-logitboost|_ .. rubric:: Examples: - `Single-Process LogitBoost Classification - `__ + `__ .. autoclass:: daal4py.logitboost_training :members: compute @@ -168,7 +168,7 @@ Parameters and semantics are described in |onedal-dg-classification-weak-learner .. rubric:: Examples: - `Single-Process Stump Weak Learner Classification - `__ + `__ .. autoclass:: daal4py.stump_classification_training :members: compute @@ -187,9 +187,9 @@ Parameters and semantics are described in |onedal-dg-naive-bayes|_. .. rubric:: Examples: -- `Single-Process Naive Bayes `__ -- `Streaming Naive Bayes `__ -- `Multi-Process Naive Bayes `__ +- `Single-Process Naive Bayes `__ +- `Streaming Naive Bayes `__ +- `Multi-Process Naive Bayes `__ .. autoclass:: daal4py.multinomial_naive_bayes_training :members: compute @@ -211,7 +211,7 @@ Note: For the labels parameter, data is formatted as -1s and 1s .. rubric:: Examples: - `Single-Process SVM - `__ + `__ .. autoclass:: daal4py.svm_training :members: compute @@ -231,9 +231,9 @@ Parameters and semantics are described in |onedal-dg-logistic-regression|_. .. rubric:: Examples: - `Single-Process Binary Class Logistic Regression - `__ + `__ - `Single-Process Logistic Regression - `__ + `__ .. autoclass:: daal4py.logistic_regression_training :members: compute @@ -257,7 +257,7 @@ Parameters and semantics are described in |onedal-dg-regression-decision-forest| .. rubric:: Examples: - `Single-Process Decision Forest Regression - `__ + `__ .. autoclass:: daal4py.decision_forest_regression_training :members: compute @@ -277,7 +277,7 @@ Parameters and semantics are described in |onedal-dg-regression-decision-tree|_. .. rubric:: Examples: - `Single-Process Decision Tree Regression - `__ + `__ .. autoclass:: daal4py.decision_tree_regression_training :members: compute @@ -297,7 +297,7 @@ Parameters and semantics are described in |onedal-dg-regression-gradient-boosted .. rubric:: Examples: - `Single-Process Boosted Regression Regression - `__ + `__ .. autoclass:: daal4py.gbt_regression_training :members: compute @@ -316,9 +316,9 @@ Parameters and semantics are described in |onedal-dg-linear-regression|_. .. rubric:: Examples: -- `Single-Process Linear Regression `__ -- `Streaming Linear Regression `__ -- `Multi-Process Linear Regression `__ +- `Single-Process Linear Regression `__ +- `Streaming Linear Regression `__ +- `Multi-Process Linear Regression `__ .. autoclass:: daal4py.linear_regression_training :members: compute @@ -337,7 +337,7 @@ Parameters and semantics are described in |onedal-dg-least-absolute-shrinkage-an .. rubric:: Examples: -- `Single-Process LASSO Regression `__ +- `Single-Process LASSO Regression `__ .. autoclass:: daal4py.lasso_regression_training :members: compute @@ -356,9 +356,9 @@ Parameters and semantics are described in |onedal-dg-ridge-regression|_. .. rubric:: Examples: -- `Single-Process Ridge Regression `__ -- `Streaming Ridge Regression `__ -- `Multi-Process Ridge Regression `__ +- `Single-Process Ridge Regression `__ +- `Streaming Ridge Regression `__ +- `Multi-Process Ridge Regression `__ .. autoclass:: daal4py.ridge_regression_training :members: compute @@ -378,7 +378,7 @@ Parameters and semantics are described in |onedal-dg-regression-stump|_. .. rubric:: Examples: - `Single-Process Stump Regression - `__ + `__ .. autoclass:: daal4py.stump_regression_training :members: compute @@ -397,8 +397,8 @@ Parameters and semantics are described in |onedal-dg-pca|_. .. rubric:: Examples: -- `Single-Process PCA `__ -- `Multi-Process PCA `__ +- `Single-Process PCA `__ +- `Multi-Process PCA `__ .. autoclass:: daal4py.pca :members: compute @@ -411,7 +411,7 @@ Parameters and semantics are described in |onedal-dg-pca-transform|_. .. rubric:: Examples: -- `Single-Process PCA Transform `__ +- `Single-Process PCA Transform `__ .. autoclass:: daal4py.pca_transform :members: compute @@ -424,8 +424,8 @@ Parameters and semantics are described in |onedal-dg-k-means-clustering|_. .. rubric:: Examples: -- `Single-Process K-Means `__ -- `Multi-Process K-Means `__ +- `Single-Process K-Means `__ +- `Multi-Process K-Means `__ K-Means Initialization ^^^^^^^^^^^^^^^^^^^^^^ @@ -451,7 +451,7 @@ Parameters and semantics are described in |onedal-dg-density-based-spatial-clust .. rubric:: Examples: -- `Single-Process DBSCAN `__ +- `Single-Process DBSCAN `__ .. autoclass:: daal4py.dbscan :members: compute @@ -466,7 +466,7 @@ Parameters and semantics are described in |onedal-dg-multivariate-outlier-detect .. rubric:: Examples: -- `Single-Process Multivariate Outlier Detection `__ +- `Single-Process Multivariate Outlier Detection `__ .. autoclass:: daal4py.multivariate_outlier_detection :members: compute @@ -479,7 +479,7 @@ Parameters and semantics are described in |onedal-dg-univariate-outlier-detectio .. rubric:: Examples: -- `Single-Process Univariate Outlier Detection `__ +- `Single-Process Univariate Outlier Detection `__ .. autoclass:: daal4py.univariate_outlier_detection :members: compute @@ -492,7 +492,7 @@ Parameters and semantics are described in |onedal-dg-multivariate-bacon-outlier- .. rubric:: Examples: -- `Single-Process Bacon Outlier Detection `__ +- `Single-Process Bacon Outlier Detection `__ .. autoclass:: daal4py.bacon_outlier_detection :members: compute @@ -508,9 +508,9 @@ Mean Squared Error Algorithm (MSE) Parameters and semantics are described in |onedal-dg-mse|_. .. rubric:: Examples: -- `In Adagrad `__ -- `In LBFGS `__ -- `In SGD `__ +- `In Adagrad `__ +- `In LBFGS `__ +- `In SGD `__ .. autoclass:: daal4py.optimization_solver_mse :members: compute, setup @@ -522,7 +522,7 @@ Logistic Loss Parameters and semantics are described in |onedal-dg-logistic-loss|_. .. rubric:: Examples: -- `In SGD `__ +- `In SGD `__ .. autoclass:: daal4py.optimization_solver_logistic_loss :members: compute, setup @@ -534,7 +534,7 @@ Cross-entropy Loss Parameters and semantics are described in |onedal-dg-cross-entropy-loss|_. .. rubric:: Examples: -- `In LBFGS `__ +- `In LBFGS `__ .. autoclass:: daal4py.optimization_solver_cross_entropy_loss :members: compute, setup @@ -548,7 +548,7 @@ Stochastic Gradient Descent Algorithm Parameters and semantics are described in |onedal-dg-sgd|_. .. rubric:: Examples: -- `Using Logistic Loss `__ +- `Using Logistic Loss `__ - `Using MSE `__ .. autoclass:: daal4py.optimization_solver_sgd @@ -561,7 +561,7 @@ Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm Parameters and semantics are described in |onedal-dg-lbfgs|_. .. rubric:: Examples: -- `Using MSE `__ +- `Using MSE `__ .. autoclass:: daal4py.optimization_solver_lbfgs :members: compute @@ -573,7 +573,7 @@ Adaptive Subgradient Method Parameters and semantics are described in |onedal-dg-adagrad|_. .. rubric:: Examples: -- `Using MSE `__ +- `Using MSE `__ .. autoclass:: daal4py.optimization_solver_adagrad :members: compute @@ -585,7 +585,7 @@ Stochastic Average Gradient Descent Parameters and semantics are described in |onedal-dg-stochastic-average-gradient-descent-saga|_. .. rubric:: Examples: -- `Single Proces saga-logistc_loss `__ +- `Single Proces saga-logistc_loss `__ .. autoclass:: daal4py.optimization_solver_saga :members: compute @@ -600,7 +600,7 @@ Parameters and semantics are described in |onedal-dg-cosine-distance|_. .. rubric:: Examples: -- `Single-Process Cosine Distance `__ +- `Single-Process Cosine Distance `__ .. autoclass:: daal4py.cosine_distance :members: compute @@ -613,7 +613,7 @@ Parameters and semantics are described in |onedal-dg-correlation-distance|_. .. rubric:: Examples: -- `Single-Process Correlation Distance `__ +- `Single-Process Correlation Distance `__ .. autoclass:: daal4py.correlation_distance :members: compute @@ -630,7 +630,7 @@ Parameters and semantics are described in |onedal-dg-expectation-maximization-in .. rubric:: Examples: -- `Single-Process Expectation-Maximization `__ +- `Single-Process Expectation-Maximization `__ .. autoclass:: daal4py.em_gmm_init :members: compute @@ -643,7 +643,7 @@ Parameters and semantics are described in |onedal-dg-expectation-maximization-fo .. rubric:: Examples: -- `Single-Process Expectation-Maximization `__ +- `Single-Process Expectation-Maximization `__ .. autoclass:: daal4py.em_gmm :members: compute @@ -660,8 +660,8 @@ Parameters and semantics are described in |onedal-dg-qr-decomposition-without-pi .. rubric:: Examples: -- `Single-Process QR `__ -- `Streaming QR `__ +- `Single-Process QR `__ +- `Streaming QR `__ .. autoclass:: daal4py.qr :members: compute @@ -674,7 +674,7 @@ Parameters and semantics are described in |onedal-dg-pivoted-qr-decomposition|_. .. rubric:: Examples: -- `Single-Process Pivoted QR `__ +- `Single-Process Pivoted QR `__ .. autoclass:: daal4py.pivoted_qr :members: compute @@ -691,7 +691,7 @@ Parameters and semantics are described in |onedal-dg-z-score|_. .. rubric:: Examples: -- `Single-Process Z-Score Normalization `__ +- `Single-Process Z-Score Normalization `__ .. autoclass:: daal4py.normalization_zscore :members: compute @@ -704,7 +704,7 @@ Parameters and semantics are described in |onedal-dg-min-max|_. .. rubric:: Examples: -- `Single-Process Min-Max Normalization `__ +- `Single-Process Min-Max Normalization `__ .. autoclass:: daal4py.normalization_minmax :members: compute @@ -755,7 +755,7 @@ Parameters and semantics are described in |onedal-dg-bernoulli-distribution|_. .. rubric:: Examples: -- `Single-Process Bernoulli Distribution `__ +- `Single-Process Bernoulli Distribution `__ .. autoclass:: daal4py.distributions_bernoulli :members: compute @@ -768,7 +768,7 @@ Parameters and semantics are described in |onedal-dg-normal-distribution|_. .. rubric:: Examples: -- `Single-Process Normal Distribution `__ +- `Single-Process Normal Distribution `__ .. autoclass:: daal4py.distributions_normal :members: compute @@ -781,7 +781,7 @@ Parameters and semantics are described in |onedal-dg-uniform-distribution|_. .. rubric:: Examples: -- `Single-Process Uniform Distribution `__ +- `Single-Process Uniform Distribution `__ .. autoclass:: daal4py.distributions_uniform :members: compute @@ -794,7 +794,7 @@ Parameters and semantics are described in |onedal-dg-association-rules|_. .. rubric:: Examples: -- `Single-Process Association Rules `__ +- `Single-Process Association Rules `__ .. autoclass:: daal4py.association_rules :members: compute @@ -807,7 +807,7 @@ Parameters and semantics are described in |onedal-dg-cholesky-decomposition|_. .. rubric:: Examples: -- `Single-Process Cholesky `__ +- `Single-Process Cholesky `__ .. autoclass:: daal4py.cholesky :members: compute @@ -820,9 +820,9 @@ Parameters and semantics are described in |onedal-dg-correlation-and-variance-co .. rubric:: Examples: -- `Single-Process Covariance `__ -- `Streaming Covariance `__ -- `Multi-Process Covariance `__ +- `Single-Process Covariance `__ +- `Streaming Covariance `__ +- `Multi-Process Covariance `__ .. autoclass:: daal4py.covariance :members: compute @@ -835,7 +835,7 @@ Parameters and semantics are described in |onedal-dg-implicit-alternating-least- .. rubric:: Examples: -- `Single-Process implicit ALS `__ +- `Single-Process implicit ALS `__ .. autoclass:: daal4py.implicit_als_training :members: compute @@ -854,9 +854,9 @@ Parameters and semantics are described in |onedal-dg-moments-of-low-order|_. .. rubric:: Examples: -- `Single-Process Low Order Moments `__ -- `Streaming Low Order Moments `__ -- `Multi-Process Low Order Moments `__ +- `Single-Process Low Order Moments `__ +- `Streaming Low Order Moments `__ +- `Multi-Process Low Order Moments `__ .. autoclass:: daal4py.low_order_moments :members: compute @@ -869,7 +869,7 @@ Parameters and semantics are described in |onedal-dg-quantiles|_. .. rubric:: Examples: -- `Single-Process Quantiles `__ +- `Single-Process Quantiles `__ .. autoclass:: daal4py.quantiles :members: compute @@ -882,9 +882,9 @@ Parameters and semantics are described in |onedal-dg-svd|_. .. rubric:: Examples: -- `Single-Process SVD `__ -- `Streaming SVD `__ -- `Multi-Process SVD `__ +- `Single-Process SVD `__ +- `Streaming SVD `__ +- `Multi-Process SVD `__ .. autoclass:: daal4py.svd :members: compute @@ -897,7 +897,7 @@ Parameters and semantics are described in |onedal-dg-sorting|_. .. rubric:: Examples: -- `Single-Process Sorting `__ +- `Single-Process Sorting `__ .. autoclass:: daal4py.sorting :members: compute @@ -910,12 +910,12 @@ Trees .. rubric:: Examples: -- `Decision Forest Regression `__ -- `Decision Forest Classification `__ -- `Decision Tree Regression `__ -- `Decision Tree Classification `__ -- `Gradient Boosted Trees Regression `__ -- `Gradient Boosted Trees Classification `__ +- `Decision Forest Regression `__ +- `Decision Forest Classification `__ +- `Decision Tree Regression `__ +- `Decision Tree Classification `__ +- `Gradient Boosted Trees Regression `__ +- `Gradient Boosted Trees Classification `__ .. Link replacements diff --git a/daal4py/_sources/examples.rst.txt b/daal4py/_sources/examples.rst.txt index 25e581a..951c694 100755 --- a/daal4py/_sources/examples.rst.txt +++ b/daal4py/_sources/examples.rst.txt @@ -9,148 +9,148 @@ Data Science examples Jupyter Notebooks -- `Linear Regression `_ +- `Linear Regression `_ General usage ------------- Principal Component Analysis (PCA) Transform -- `Single-Process PCA `_ -- `Multi-Process PCA `_ +- `Single-Process PCA `_ +- `Multi-Process PCA `_ Singular Value Decomposition (SVD) -- `Single-Process PCA Transform `_ +- `Single-Process PCA Transform `_ -- `Single-Process SVD `_ -- `Streaming SVD `_ -- `Multi-Process SVD `_ +- `Single-Process SVD `_ +- `Streaming SVD `_ +- `Multi-Process SVD `_ Moments of Low Order -- `Single-Process Low Order Moments `_ -- `Streaming Low Order Moments `_ -- `Multi-Process Low Order Moments `_ +- `Single-Process Low Order Moments `_ +- `Streaming Low Order Moments `_ +- `Multi-Process Low Order Moments `_ Correlation and Variance-Covariance Matrices -- `Single-Process Covariance `_ -- `Streaming Covariance `_ -- `Multi-Process Covariance `_ +- `Single-Process Covariance `_ +- `Streaming Covariance `_ +- `Multi-Process Covariance `_ Decision Forest Classification - `Single-Process Decision Forest Classification - `_ + `_ Decision Tree Classification - `Single-Process Decision Tree Classification - `_ + `_ Gradient Boosted Classification - `Single-Process Gradient Boosted Classification - `_ + `_ k-Nearest Neighbors (kNN) - `Single-Process kNN - `_ + `_ Multinomial Naive Bayes -- `Single-Process Naive Bayes `_ -- `Streaming Naive Bayes `_ -- `Multi-Process Naive Bayes `_ +- `Single-Process Naive Bayes `_ +- `Streaming Naive Bayes `_ +- `Multi-Process Naive Bayes `_ Support Vector Machine (SVM) - `Single-Process SVM - `_ + `_ Logistic Regression - `Single-Process Binary Class Logistic Regression - `_ + `_ - `Single-Process Logistic Regression - `_ + `_ Decision Forest Regression - `Single-Process Decision Forest Regression - `_ + `_ - `Single-Process Decision Tree Regression - `_ + `_ Gradient Boosted Regression - `Single-Process Boosted Regression - `_ + `_ Linear Regression -- `Single-Process Linear Regression `_ -- `Streaming Linear Regression `_ -- `Multi-Process Linear Regression `_ +- `Single-Process Linear Regression `_ +- `Streaming Linear Regression `_ +- `Multi-Process Linear Regression `_ Ridge Regression -- `Single-Process Ridge Regression `_ -- `Streaming Ridge Regression `_ -- `Multi-Process Ridge Regression `_ +- `Single-Process Ridge Regression `_ +- `Streaming Ridge Regression `_ +- `Multi-Process Ridge Regression `_ K-Means Clustering -- `Single-Process K-Means `_ -- `Multi-Process K-Means `_ +- `Single-Process K-Means `_ +- `Multi-Process K-Means `_ Multivariate Outlier Detection -- `Single-Process Multivariate Outlier Detection `_ +- `Single-Process Multivariate Outlier Detection `_ Univariate Outlier Detection -- `Single-Process Univariate Outlier Detection `_ +- `Single-Process Univariate Outlier Detection `_ Optimization Solvers-Mean Squared Error Algorithm (MSE) -- `MSE In Adagrad `_ -- `MSE In LBFGS `_ -- `MSE In SGD `_ +- `MSE In Adagrad `_ +- `MSE In LBFGS `_ +- `MSE In SGD `_ Logistic Loss -- `Logistic Loss SGD `_ +- `Logistic Loss SGD `_ Stochastic Gradient Descent Algorithm -- `Stochastic Gradient Descent Algorithm Using Logistic Loss `_ +- `Stochastic Gradient Descent Algorithm Using Logistic Loss `_ - `Stochastic Gradient Descent Algorithm Using MSE `_ Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm -- `Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm - Using MSE `_ +- `Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm - Using MSE `_ Adaptive Subgradient Method -- `Adaptive Subgradient Method Using MSE `_ +- `Adaptive Subgradient Method Using MSE `_ Cosine Distance Matrix -- `Single-Process Cosine Distance `_ +- `Single-Process Cosine Distance `_ Correlation Distance Matrix -- `Single-Process Correlation Distance `_ +- `Single-Process Correlation Distance `_ Trees -- `Decision Forest Regression `_ -- `Decision Forest Classification `_ -- `Decision Tree Regression `_ -- `Decision Tree Classification `_ -- `Gradient Boosted Trees Regression `_ -- `Gradient Boosted Trees Classification `_ +- `Decision Forest Regression `_ +- `Decision Forest Classification `_ +- `Decision Tree Regression `_ +- `Decision Tree Classification `_ +- `Gradient Boosted Trees Regression `_ +- `Gradient Boosted Trees Classification `_ diff --git a/daal4py/_sources/scaling.rst.txt b/daal4py/_sources/scaling.rst.txt index 2ca6c7d..d41c670 100755 --- a/daal4py/_sources/scaling.rst.txt +++ b/daal4py/_sources/scaling.rst.txt @@ -57,36 +57,36 @@ The following algorithms support distribution: - PCA (pca) - - `PCA `_ + - `PCA `_ - SVD (svd) - - `SVD `_ + - `SVD `_ - Linear Regression Training (linear_regression_training) - - `Linear Regression `_ + - `Linear Regression `_ - Ridge Regression Training (ridge_regression_training) - - `Ridge Regression `_ + - `Ridge Regression `_ - Multinomial Naive Bayes Training (multinomial_naive_bayes_training) - - `Naive Bayes `_ + - `Naive Bayes `_ - K-Means (kmeans_init and kmeans) - - `K-Means `_ + - `K-Means `_ - Correlation and Variance-Covariance Matrices (covariance) - - `Covariance `_ + - `Covariance `_ - Moments of Low Order (low_order_moments) - - `Low Order Moments `_ + - `Low Order Moments `_ - QR Decomposition (qr) - - `QR `_ + - `QR `_ diff --git a/daal4py/_sources/streaming.rst.txt b/daal4py/_sources/streaming.rst.txt index 8b31ac7..3436b84 100755 --- a/daal4py/_sources/streaming.rst.txt +++ b/daal4py/_sources/streaming.rst.txt @@ -29,7 +29,7 @@ daal4py's streaming mode is as easy as follows: The streaming algorithms also accept arrays and DataFrames as input, e.g. the data can come from a stream rather than from multiple files. Here is an example which simulates a data stream using a generator which reads a file in chunks: -`SVD reading stream of data `_ +`SVD reading stream of data `_ Supported Algorithms and Examples --------------------------------- @@ -37,20 +37,20 @@ The following algorithms support streaming: - SVD (svd) - - `SVD `_ + - `SVD `_ - Linear Regression Training (linear_regression_training) - - `Linear Regression `_ + - `Linear Regression `_ - Ridge Regression Training (ridge_regression_training) - - `Ridge Regression `_ + - `Ridge Regression `_ - Multinomial Naive Bayes Training (multinomial_naive_bayes_training) - - `Naive Bayes `_ + - `Naive Bayes `_ - Moments of Low Order - - `Low Order Moments `_ + - `Low Order Moments `_ diff --git a/daal4py/algorithms.html b/daal4py/algorithms.html index 2083a42..42ce848 100755 --- a/daal4py/algorithms.html +++ b/daal4py/algorithms.html @@ -275,7 +275,7 @@

Decision Forest ClassificationIntel(R) oneAPI Data Analytics Library Classification Decision Forest.

Examples:

@@ -494,7 +494,7 @@

Decision Tree ClassificationIntel(R) oneAPI Data Analytics Library Classification Decision Tree.

Examples:

@@ -657,7 +657,7 @@

Gradient Boosted ClassificationIntel(R) oneAPI Data Analytics Library Classification Gradient Boosted Tree.

Examples:

@@ -885,7 +885,7 @@

k-Nearest Neighbors (kNN)Intel(R) oneAPI Data Analytics Library k-Nearest Neighbors (kNN).

Examples:

@@ -1203,7 +1203,7 @@

AdaBoost ClassificationParameters and semantics are described in Intel(R) oneAPI Data Analytics Library Classification AdaBoost.

Examples:

@@ -1406,7 +1406,7 @@

BrownBoost ClassificationIntel(R) oneAPI Data Analytics Library Classification BrownBoost.

Examples:

@@ -1601,7 +1601,7 @@

LogitBoost ClassificationIntel(R) oneAPI Data Analytics Library Classification LogitBoost.

Examples:

@@ -1794,7 +1794,7 @@

Stump Weak Learner ClassificationIntel(R) oneAPI Data Analytics Library Classification Weak Learner Stump.

Examples:

@@ -1997,9 +1997,9 @@

Multinomial Naive BayesParameters and semantics are described in Intel(R) oneAPI Data Analytics Library Naive Bayes.

Examples:

@@ -2187,7 +2187,7 @@

Support Vector Machine (SVM)Examples:

@@ -2394,8 +2394,8 @@

Logistic RegressionIntel(R) oneAPI Data Analytics Library Logistic Regression.

Examples:

@@ -2584,7 +2584,7 @@

Decision Forest RegressionIntel(R) oneAPI Data Analytics Library Regression Decision Forest.

Examples:

@@ -2758,7 +2758,7 @@

Decision Tree RegressionIntel(R) oneAPI Data Analytics Library Regression Decision Tree.

Examples:

@@ -2883,7 +2883,7 @@

Gradient Boosted RegressionIntel(R) oneAPI Data Analytics Library Regression Gradient Boosted Tree.

Examples:

@@ -3076,9 +3076,9 @@

Linear RegressionIntel(R) oneAPI Data Analytics Library Linear Regression.

Examples:

@@ -3237,7 +3237,7 @@

Least Absolute Shrinkage and Selection OperatorIntel(R) oneAPI Data Analytics Library Least Absolute Shrinkage and Selection Operator.

Examples:

@@ -3410,9 +3410,9 @@

Ridge RegressionIntel(R) oneAPI Data Analytics Library Ridge Regression.

Examples:

@@ -3572,7 +3572,7 @@

Stump RegressionIntel(R) oneAPI Data Analytics Library Regression Stump.

Examples:

@@ -3742,8 +3742,8 @@

Principal Component Analysis (PCA)Intel(R) oneAPI Data Analytics Library PCA.

Examples:

@@ -3841,7 +3841,7 @@

Principal Component Analysis (PCA) TransformIntel(R) oneAPI Data Analytics Library PCA Transform.

Examples:

@@ -3898,8 +3898,8 @@

K-Means ClusteringIntel(R) oneAPI Data Analytics Library K-Means Clustering.

Examples:

K-Means Initialization

@@ -4047,7 +4047,7 @@

Density-Based Spatial Clustering of Applications with NoiseIntel(R) oneAPI Data Analytics Library Density-Based Spatial Clustering of Applications with Noise.

Examples:

@@ -4142,7 +4142,7 @@

Multivariate Outlier DetectionIntel(R) oneAPI Data Analytics Library Multivariate Outlier Detection.

Examples:

@@ -4198,7 +4198,7 @@

Univariate Outlier DetectionIntel(R) oneAPI Data Analytics Library Univariate Outlier Detection.

Examples:

@@ -4254,7 +4254,7 @@

Multivariate Bacon Outlier DetectionIntel(R) oneAPI Data Analytics Library Multivariate Bacon Outlier Detection.

Examples:

@@ -4313,9 +4313,9 @@

Mean Squared Error Algorithm (MSE)Intel(R) oneAPI Data Analytics Library MSE.

Examples:

@@ -4389,7 +4389,7 @@

Logistic LossIntel(R) oneAPI Data Analytics Library Logistic Loss.

Examples:

@@ -4459,7 +4459,7 @@

Cross-entropy LossIntel(R) oneAPI Data Analytics Library Cross Entropy Loss.

Examples:

@@ -4533,7 +4533,7 @@

Stochastic Gradient Descent AlgorithmIntel(R) oneAPI Data Analytics Library SGD.

Examples:

@@ -4606,7 +4606,7 @@

Limited-Memory Broyden-Fletcher-Goldfarb-Shanno AlgorithmIntel(R) oneAPI Data Analytics Library LBFGS.

Examples:

@@ -4679,7 +4679,7 @@

Adaptive Subgradient MethodIntel(R) oneAPI Data Analytics Library AdaGrad.

Examples:

@@ -4749,7 +4749,7 @@

Stochastic Average Gradient DescentIntel(R) oneAPI Data Analytics Library Stochastic Average Gradient Descent SAGA.

Examples:

@@ -4835,7 +4835,7 @@

Cosine Distance MatrixParameters and semantics are described in Intel(R) oneAPI Data Analytics Library Cosine Distance.

Examples:

@@ -4886,7 +4886,7 @@

Correlation Distance MatrixIntel(R) oneAPI Data Analytics Library Correlation Distance.

Examples:

@@ -4941,7 +4941,7 @@

Initialization for the Gaussian Mixture ModelIntel(R) oneAPI Data Analytics Library Expectation-Maximization Initialization.

Examples:

@@ -5018,7 +5018,7 @@

EM algorithm for the Gaussian Mixture ModelIntel(R) oneAPI Data Analytics Library Expectation-Maximization for the Gaussian Mixture Model.

Examples:

@@ -5123,8 +5123,8 @@

QR Decomposition (without pivoting)Intel(R) oneAPI Data Analytics Library QR Decomposition without pivoting.

Examples:

@@ -5187,7 +5187,7 @@

Pivoted QR DecompositionIntel(R) oneAPI Data Analytics Library Pivoted QR Decomposition.

Examples:

@@ -5263,7 +5263,7 @@

Z-ScoreIntel(R) oneAPI Data Analytics Library Z-Score.

Examples:

@@ -5336,7 +5336,7 @@

Min-MaxIntel(R) oneAPI Data Analytics Library Min-Max.

Examples:

@@ -5527,7 +5527,7 @@

BernoulliIntel(R) oneAPI Data Analytics Library Bernoulli Distribution.

Examples:

@@ -5570,7 +5570,7 @@

NormalIntel(R) oneAPI Data Analytics Library Normal Distribution.

Examples:

@@ -5614,7 +5614,7 @@

UniformIntel(R) oneAPI Data Analytics Library Uniform Distribution.

Examples:

@@ -5659,7 +5659,7 @@

Association RulesIntel(R) oneAPI Data Analytics Library Association Rules.

Examples:

@@ -5759,7 +5759,7 @@

Cholesky DecompositionParameters and semantics are described in Intel(R) oneAPI Data Analytics Library Cholesky Decomposition.

Examples:

@@ -5810,9 +5810,9 @@

Correlation and Variance-Covariance MatricesIntel(R) oneAPI Data Analytics Library Correlation and Variance-Covariance Matrices.

Examples:

@@ -5886,7 +5886,7 @@

Implicit Alternating Least Squares (implicit ALS)Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Implicit Alternating Least Squares.

Examples:

@@ -6019,9 +6019,9 @@

Moments of Low OrderParameters and semantics are described in Intel(R) oneAPI Data Analytics Library Moments of Low Order.

Examples:

@@ -6165,7 +6165,7 @@

QuantilesIntel(R) oneAPI Data Analytics Library Quantiles.

Examples:

@@ -6217,9 +6217,9 @@

Singular Value Decomposition (SVD)Intel(R) oneAPI Data Analytics Library SVD.

Examples:

@@ -6294,7 +6294,7 @@

SortingIntel(R) oneAPI Data Analytics Library Sorting.

Examples:

@@ -6349,12 +6349,12 @@

Trees

Examples:

diff --git a/daal4py/examples.html b/daal4py/examples.html index 43d8988..0dc5931 100755 --- a/daal4py/examples.html +++ b/daal4py/examples.html @@ -176,139 +176,139 @@

Examples

Jupyter Notebooks

General usage

Principal Component Analysis (PCA) Transform

Singular Value Decomposition (SVD)

Moments of Low Order

Correlation and Variance-Covariance Matrices

Decision Forest Classification

Decision Tree Classification

Gradient Boosted Classification

k-Nearest Neighbors (kNN)

Multinomial Naive Bayes

Support Vector Machine (SVM)

Logistic Regression

Decision Forest Regression

Gradient Boosted Regression

Linear Regression

Ridge Regression

K-Means Clustering

Multivariate Outlier Detection

Univariate Outlier Detection

Optimization Solvers-Mean Squared Error Algorithm (MSE)

Logistic Loss

Stochastic Gradient Descent Algorithm

Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm

Adaptive Subgradient Method

Cosine Distance Matrix

Correlation Distance Matrix

Trees

diff --git a/daal4py/scaling.html b/daal4py/scaling.html index d500b9c..76c573f 100755 --- a/daal4py/scaling.html +++ b/daal4py/scaling.html @@ -223,47 +223,47 @@

Supported Algorithms and Examples
  • PCA (pca)

  • SVD (svd)

  • Linear Regression Training (linear_regression_training)

  • Ridge Regression Training (ridge_regression_training)

  • Multinomial Naive Bayes Training (multinomial_naive_bayes_training)

  • K-Means (kmeans_init and kmeans)

  • Correlation and Variance-Covariance Matrices (covariance)

  • Moments of Low Order (low_order_moments)

  • QR Decomposition (qr)

  • diff --git a/daal4py/streaming.html b/daal4py/streaming.html index f77c29f..5b5d17c 100755 --- a/daal4py/streaming.html +++ b/daal4py/streaming.html @@ -200,34 +200,34 @@

    The streaming algorithms also accept arrays and DataFrames as input, e.g. the data can come from a stream rather than from multiple files. Here is an example which simulates a data stream using a generator which reads a file in chunks: -SVD reading stream of data

    +SVD reading stream of data

    Supported Algorithms and Examples

    The following algorithms support streaming: