add missing models from MODELS constant

Author: ablaom

src/MLJMultivariateStatsInterface.jl

@@ -18,7 +18,7 @@ export LinearRegressor, RidgeRegressor, PCA, KernelPCA, ICA, PPCA, FactorAnalysi
     BayesianLDA, SubspaceLDA, BayesianSubspaceLDA
 
 # ===================================================================
-## Re-EXPORTS    
+## Re-EXPORTS
 export SimpleCovariance, CovarianceEstimator, SqEuclidean, CosineDist
 
 # ===================================================================
@@ -34,84 +34,85 @@ const FactorAnalysisResultType = MS.FactorAnalysis
 const default_kernel = (x, y) -> x'y #default kernel used in KernelPCA
 
 # Definitions of model descriptions for use in model doc-strings.
-const PCA_DESCR = """Principal component analysis. Learns a linear transformation to 
-project the data  on a lower dimensional space while preserving most of the initial 
+const PCA_DESCR = """Principal component analysis. Learns a linear transformation to
+project the data  on a lower dimensional space while preserving most of the initial
 variance.
 """
 const KPCA_DESCR = "Kernel principal component analysis."
 const ICA_DESCR = "Independent component analysis."
 const PPCA_DESCR = "Probabilistic principal component analysis"
 const FactorAnalysis_DESCR = "Factor Analysis"
-const LDA_DESCR = """Multiclass linear discriminant analysis. The algorithm learns a 
-projection matrix `P` that projects a feature matrix `Xtrain` onto a lower dimensional  
-space of dimension `out_dim` such that the trace of the transformed between-class scatter 
-matrix(`Pᵀ*Sb*P`) is maximized relative to the trace of the transformed within-class 
-scatter matrix (`Pᵀ*Sw*P`).The projection matrix is scaled such that `Pᵀ*Sw*P=I` or 
+const LDA_DESCR = """Multiclass linear discriminant analysis. The algorithm learns a
+projection matrix `P` that projects a feature matrix `Xtrain` onto a lower dimensional
+space of dimension `out_dim` such that the trace of the transformed between-class scatter
+matrix(`Pᵀ*Sb*P`) is maximized relative to the trace of the transformed within-class
+scatter matrix (`Pᵀ*Sw*P`).The projection matrix is scaled such that `Pᵀ*Sw*P=I` or
 `Pᵀ*Σw*P=I`(where `Σw` is the within-class covariance matrix) .
-Predicted class posterior probability for feature matrix `Xtest` are derived by applying  
-a softmax transformationto a matrix `Pr`, such that  rowᵢ of `Pr` contains computed 
-distances(based on a distance metric) in the transformed space of rowᵢ in `Xtest` to the 
+Predicted class posterior probability for feature matrix `Xtest` are derived by applying
+a softmax transformationto a matrix `Pr`, such that  rowᵢ of `Pr` contains computed
+distances(based on a distance metric) in the transformed space of rowᵢ in `Xtest` to the
 centroid of each class.
 """
-const BayesianLDA_DESCR = """Bayesian Multiclass linear discriminant analysis. The algorithm 
-learns a projection matrix `P` that projects a feature matrix `Xtrain` onto a lower 
-dimensional space of dimension `out_dim` such that the trace of the transformed 
-between-class scatter matrix(`Pᵀ*Sb*P`) is maximized relative to the trace of the 
-transformed within-class scatter matrix (`Pᵀ*Sw*P`). The projection matrix is scaled such 
-that `Pᵀ*Sw*P = n` or `Pᵀ*Σw*P=I` (Where `n` is the number of training samples and `Σw` 
+const BayesianLDA_DESCR = """Bayesian Multiclass linear discriminant analysis. The algorithm
+learns a projection matrix `P` that projects a feature matrix `Xtrain` onto a lower
+dimensional space of dimension `out_dim` such that the trace of the transformed
+between-class scatter matrix(`Pᵀ*Sb*P`) is maximized relative to the trace of the
+transformed within-class scatter matrix (`Pᵀ*Sw*P`). The projection matrix is scaled such
+that `Pᵀ*Sw*P = n` or `Pᵀ*Σw*P=I` (Where `n` is the number of training samples and `Σw`
 is the within-class covariance matrix).
-Predicted class posterior probability distibution are derived by applying Bayes rule with 
+Predicted class posterior probability distibution are derived by applying Bayes rule with
 a multivariate Gaussian class-conditional distribution.
 """
-const SubspaceLDA_DESCR = """Multiclass linear discriminant analysis. Suitable for high 
-dimensional data (Avoids computing scatter matrices `Sw` ,`Sb`). The algorithm learns a 
-projection matrix `P = W*L` that projects a feature matrix `Xtrain` onto a lower 
-dimensional space of dimension `nc - 1` such that the trace of the transformed 
-between-class scatter matrix(`Pᵀ*Sb*P`) is maximized relative to the trace of the 
-transformed within-class scatter matrix (`Pᵀ*Sw*P`). The projection matrix is scaled such 
-that `Pᵀ*Sw*P = mult*I` or `Pᵀ*Σw*P=mult/(n-nc)*I` (where `n` is the number of training 
-samples, mult` is  one of `n` or `1` depending on whether `Sb` is normalized, `Σw` is the 
-within-class covariance matrix, and `nc` is the number of unique classes in `y`) and also 
+const SubspaceLDA_DESCR = """Multiclass linear discriminant analysis. Suitable for high
+dimensional data (Avoids computing scatter matrices `Sw` ,`Sb`). The algorithm learns a
+projection matrix `P = W*L` that projects a feature matrix `Xtrain` onto a lower
+dimensional space of dimension `nc - 1` such that the trace of the transformed
+between-class scatter matrix(`Pᵀ*Sb*P`) is maximized relative to the trace of the
+transformed within-class scatter matrix (`Pᵀ*Sw*P`). The projection matrix is scaled such
+that `Pᵀ*Sw*P = mult*I` or `Pᵀ*Σw*P=mult/(n-nc)*I` (where `n` is the number of training
+samples, mult` is  one of `n` or `1` depending on whether `Sb` is normalized, `Σw` is the
+within-class covariance matrix, and `nc` is the number of unique classes in `y`) and also
 obeys `Wᵀ*Sb*p = λ*Wᵀ*Sw*p`, for every column `p` in `P`.
-Predicted class posterior probability for feature matrix `Xtest` are derived by applying a 
-softmax transformation to a matrix `Pr`, such that  rowᵢ of `Pr` contains computed 
-distances(based on a distance metric) in the transformed space of rowᵢ in `Xtest` to the 
+Predicted class posterior probability for feature matrix `Xtest` are derived by applying a
+softmax transformation to a matrix `Pr`, such that  rowᵢ of `Pr` contains computed
+distances(based on a distance metric) in the transformed space of rowᵢ in `Xtest` to the
 centroid of each class.
 """
-const BayesianSubspaceLDA_DESCR = """Bayesian Multiclass linear discriminant analysis. 
-Suitable for high dimensional data (Avoids computing scatter matrices `Sw` ,`Sb`). The 
-algorithm learns a projection matrix `P = W*L` (`Sw`), that projects a feature matrix 
-`Xtrain` onto a lower dimensional space of dimension `nc-1` such that the trace of the 
-transformed between-class scatter matrix(`Pᵀ*Sb*P`) is maximized relative to the trace 
-of the transformed within-class scatter matrix (`Pᵀ*Sw*P`). The projection matrix is 
-scaled such that `Pᵀ*Sw*P = mult*I` or `Pᵀ*Σw*P=mult/(n-nc)*I` (where `n` is the number of 
-training samples, `mult` is  one of `n` or `1` depending on whether `Sb` is normalized, 
-`Σw` is the within-class covariance matrix, and `nc` is the number of unique classes in 
+const BayesianSubspaceLDA_DESCR = """Bayesian Multiclass linear discriminant analysis.
+Suitable for high dimensional data (Avoids computing scatter matrices `Sw` ,`Sb`). The
+algorithm learns a projection matrix `P = W*L` (`Sw`), that projects a feature matrix
+`Xtrain` onto a lower dimensional space of dimension `nc-1` such that the trace of the
+transformed between-class scatter matrix(`Pᵀ*Sb*P`) is maximized relative to the trace
+of the transformed within-class scatter matrix (`Pᵀ*Sw*P`). The projection matrix is
+scaled such that `Pᵀ*Sw*P = mult*I` or `Pᵀ*Σw*P=mult/(n-nc)*I` (where `n` is the number of
+training samples, `mult` is  one of `n` or `1` depending on whether `Sb` is normalized,
+`Σw` is the within-class covariance matrix, and `nc` is the number of unique classes in
 `y`) and also obeys `Wᵀ*Sb*p = λ*Wᵀ*Sw*p`, for every column `p` in `P`.
-Posterior class probability distibution are derived by applying Bayes rule with a 
+Posterior class probability distibution are derived by applying Bayes rule with a
 multivariate Gaussian class-conditional distribution
 """
-const LINEAR_DESCR = """Linear regression. Learns a linear combination(s) of given 
+const LINEAR_DESCR = """Linear regression. Learns a linear combination(s) of given
 variables to fit the responses by minimizing the squared error between.
 """
-const RIDGE_DESCR = """Ridge regressor with regularization parameter lambda. Learns a 
+const RIDGE_DESCR = """Ridge regressor with regularization parameter lambda. Learns a
 linear regression with a penalty on the l2 norm of the coefficients.
 """
 
 const PKG = "MLJMultivariateStatsInterface"
 
 # ===================================================================
 # Includes
-include("models/decompostion_models.jl")
+include("models/decomposition_models.jl")
 include("models/discriminant_analysis.jl")
 include("models/linear_models.jl")
 include("utils.jl")
 
 # ===================================================================
 # List of all models interfaced
 const MODELS = (
-    RidgeRegressor, PCA, KernelPCA, ICA, LDA, BayesianLDA, SubspaceLDA,
-    BayesianSubspaceLDA
+    LinearRegressor, RidgeRegressor, PCA, KernelPCA, ICA, LDA,
+    BayesianLDA, SubspaceLDA,
+    BayesianSubspaceLDA, FactorAnalysis, PPCA
 )
 
 # ====================================================================