add linear least squares and ridge regression

This add linear ridge regression and a convenience class CLeastSquaresRegression calling CLinearRidgeRegression with regularization parameter tau=0. To not cause confusion KRR is renamed to KernelRidgeRegression throughout examples/code.
shogun-toolbox · Feb 14, 2012 · 3e3db13 · 3e3db13
1 parent 3649f3b
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diff --git a/examples/undocumented/cmdline_static/regression_krr.sg b/examples/undocumented/cmdline_static/regression_krr.sg
@@ -1,5 +1,5 @@
 % kernel ridge regression
-print KRR
+print Kernel Ridge Regression
 set_kernel GAUSSIAN REAL 10 2.1
 set_features TRAIN ../data/fm_train_real.dat
 set_labels TRAIN ../data/label_train_twoclass.dat

diff --git a/examples/undocumented/csharp_modular/regression_krr_modular.cs b/examples/undocumented/csharp_modular/regression_krr_modular.cs
@@ -17,7 +17,7 @@ public class regression_krr_modular {
 
 		Labels labels = new Labels(trainlab);
 
-		KRR krr = new KRR(tau, kernel, labels);
+		KernelRidgeRegression krr = new KernelRidgeRegression(tau, kernel, labels);
 		krr.train(feats_train);
 
 		kernel.init(feats_train, feats_test);

diff --git a/examples/undocumented/java_modular/regression_krr_modular.java b/examples/undocumented/java_modular/regression_krr_modular.java
@@ -22,7 +22,7 @@ public static void main(String argv[]) {
 
 		Labels labels = new Labels(trainlab);
 
-		KRR krr = new KRR(tau, kernel, labels);
+		KernelRidgeRegression krr = new KernelRidgeRegression(tau, kernel, labels);
 		krr.train(feats_train);
 
 		kernel.init(feats_train, feats_test);

diff --git a/examples/undocumented/libshogun/evaluation_cross_validation_regression.cpp b/examples/undocumented/libshogun/evaluation_cross_validation_regression.cpp
@@ -12,7 +12,7 @@
 #include <shogun/features/SimpleFeatures.h>
 #include <shogun/features/Labels.h>
 #include <shogun/kernel/LinearKernel.h>
-#include <shogun/regression/KRR.h>
+#include <shogun/regression/KernelRidgeRegression.h>
 #include <shogun/evaluation/CrossValidation.h>
 #include <shogun/evaluation/CrossValidationSplitting.h>
 #include <shogun/evaluation/MeanSquaredError.h>
@@ -57,7 +57,7 @@ void test_cross_validation()
 
 	/* kernel ridge regression*/
 	float64_t tau=0.0001;
-	CKRR* krr=new CKRR(tau, kernel, labels);
+	CKernelRidgeRegression* krr=new CKernelRidgeRegression(tau, kernel, labels);
 
 	/* evaluation criterion */
 	CMeanSquaredError* eval_crit=

diff --git a/examples/undocumented/libshogun/modelselection_grid_search_krr.cpp b/examples/undocumented/libshogun/modelselection_grid_search_krr.cpp
@@ -13,7 +13,7 @@
 #include <shogun/features/Labels.h>
 #include <shogun/kernel/GaussianKernel.h>
 #include <shogun/kernel/PolyKernel.h>
-#include <shogun/regression/KRR.h>
+#include <shogun/regression/KernelRidgeRegression.h>
 #include <shogun/evaluation/CrossValidation.h>
 #include <shogun/evaluation/CrossValidationSplitting.h>
 #include <shogun/evaluation/MeanSquaredError.h>
@@ -97,7 +97,7 @@ void test_cross_validation()
 	CLabels* labels=new CLabels(lab);
 
 	/* kernel ridge regression, only set labels for now, rest does not matter */
-	CKRR* krr=new CKRR(0, NULL, labels);
+	CKernelRidgeRegression* krr=new CKernelRidgeRegression(0, NULL, labels);
 
 	/* evaluation criterion */
 	CMeanSquaredError* eval_crit=

diff --git a/examples/undocumented/lua_modular/regression_krr_modular.lua b/examples/undocumented/lua_modular/regression_krr_modular.lua
@@ -16,13 +16,13 @@ function regression_krr_modular (fm_train,fm_test,label_train,width,tau)
 
 	labels=modshogun.Labels(label_train)
 
-	krr=modshogun.KRR(tau, kernel, labels)
+	krr=modshogun.KernelRidgeRegression(tau, kernel, labels)
 	krr:train(feats_train)
 
 	kernel:init(feats_train, feats_test)
 	out = krr:apply():get_labels()
 	return out,kernel,krr
 end
 
-print 'KRR'
+print 'KernelRidgeRegression'
 regression_krr_modular(unpack(parameter_list[1]))
diff --git a/examples/undocumented/matlab_and_octave/regression_krr.m b/examples/undocumented/matlab_and_octave/regression_krr.m
@@ -8,8 +8,8 @@
 fm_train=load_matrix('../data/fm_train_real.dat');
 fm_test=load_matrix('../data/fm_test_real.dat');
 
-% KRR
-disp('KRR');
+% KernelRidgeRegression
+disp('KernelRidgeRegression');
 
 tau=1.2;
 

diff --git a/...d/octave_modular/regression_krr_modular.m → ...modular/regression_kernel_ridge_modular.m b/...d/octave_modular/regression_krr_modular.m → ...modular/regression_kernel_ridge_modular.m
diff --git a/examples/undocumented/python_modular/evaluation_cross_validation_regression.py b/examples/undocumented/python_modular/evaluation_cross_validation_regression.py
@@ -25,15 +25,15 @@ def evaluation_cross_validation_regression(fm_train=traindat,fm_test=testdat,lab
     from shogun.Evaluation import CrossValidationSplitting
     from shogun.Features import Labels, RealFeatures
     from shogun.Kernel import GaussianKernel
-    from shogun.Regression import KRR
+    from shogun.Regression import KernelRidgeRegression
 
     # training data
     features=RealFeatures(fm_train)
     labels=Labels(label_train)
 
     # kernel and predictor
     kernel=GaussianKernel()
-    predictor=KRR(tau, kernel, labels)
+    predictor=KernelRidgeRegression(tau, kernel, labels)
 
     # splitting strategy for 5 fold cross-validation (for classification its better
     # to use "StratifiedCrossValidation", but here, the std x-val is used

diff --git a/...ocumented/python_modular/graphical/krr.py → ...ular/graphical/kernel_ridge_regression.py b/...ocumented/python_modular/graphical/krr.py → ...ular/graphical/kernel_ridge_regression.py
@@ -6,7 +6,7 @@
 from shogun.Kernel import *
 import util
 
-util.set_title('KRR')
+util.set_title('KernelRidgeRegression')
 
 width=20
 
@@ -22,7 +22,7 @@
 labels = util.get_labels()
 train = util.get_realfeatures(pos, neg)
 gk=GaussianKernel(train, train, width)
-krr = KRR()
+krr = KernelRidgeRegression()
 krr.set_labels(labels)
 krr.set_kernel(gk)
 krr.set_tau(1e-3)

diff --git a/...nted/python_modular/graphical/krr_sinc.py → ...graphical/kernel_ridge_regression_sinc.py b/...nted/python_modular/graphical/krr_sinc.py → ...graphical/kernel_ridge_regression_sinc.py
@@ -4,7 +4,7 @@
 from shogun.Kernel import *
 import util
 
-util.set_title('KRR on Sine')
+util.set_title('KernelRidgeRegression on Sine')
 
 
 X, Y=util.get_sinedata()
@@ -13,7 +13,7 @@
 feat=RealFeatures(X)
 lab=Labels(Y.flatten())
 gk=GaussianKernel(feat, feat, width)
-krr=KRR()
+krr=KernelRidgeRegression()
 krr.set_labels(lab)
 krr.set_kernel(gk)
 krr.set_tau(1e-6)

diff --git a/examples/undocumented/python_modular/modelselection_grid_search_krr.py b/examples/undocumented/python_modular/modelselection_grid_search_krr.py
@@ -28,7 +28,7 @@ def evaluation_cross_validation_classification(fm_train=traindat,fm_test=testdat
     from shogun.Evaluation import CrossValidationSplitting
     from shogun.Features import Labels
     from shogun.Features import RealFeatures
-    from shogun.Regression import KRR
+    from shogun.Regression import KernelRidgeRegression
     from shogun.ModelSelection import GridSearchModelSelection
     from shogun.ModelSelection import ModelSelectionParameters
 
@@ -41,7 +41,7 @@ def evaluation_cross_validation_classification(fm_train=traindat,fm_test=testdat
     labels=Labels(label_train)
 
     # predictor, set tau=0 here, doesnt matter
-    predictor=KRR()
+    predictor=KernelRidgeRegression()
 
     # splitting strategy for 5 fold cross-validation (for classification its better
     # to use "StratifiedCrossValidation", but the standard

diff --git a/.../python_modular/regression_krr_modular.py → ...odular/regression_kernel_ridge_modular.py b/.../python_modular/regression_krr_modular.py → ...odular/regression_kernel_ridge_modular.py
@@ -13,11 +13,11 @@
 
 parameter_list = [[traindat,testdat,label_traindat,0.8,1e-6],[traindat,testdat,label_traindat,0.9,1e-7]]
 
-def regression_krr_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,width=0.8,tau=1e-6):
+def regression_kernel_ridge_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,width=0.8,tau=1e-6):
 
 	from shogun.Features import Labels, RealFeatures
 	from shogun.Kernel import GaussianKernel
-	from shogun.Regression import KRR
+	from shogun.Regression import KernelRidgeRegression
 
 	feats_train=RealFeatures(fm_train)
 	feats_test=RealFeatures(fm_test)
@@ -26,7 +26,7 @@ def regression_krr_modular (fm_train=traindat,fm_test=testdat,label_train=label_
 
 	labels=Labels(label_train)
 
-	krr=KRR(tau, kernel, labels)
+	krr=KernelRidgeRegression(tau, kernel, labels)
 	krr.train(feats_train)
 
 	kernel.init(feats_train, feats_test)
@@ -38,15 +38,15 @@ def krr_short ():
 	print 'KRR_short'
 	from shogun.Features import Labels, RealFeatures
 	from shogun.Kernel import GaussianKernel
-	from shogun.Regression import KRR
+	from shogun.Regression import KernelRidgeRegression
 
 	width=0.8; tau=1e-6
-	krr=KRR(tau, GaussianKernel(0, width), Labels(label_train))
+	krr=KernelRidgeRegression(tau, GaussianKernel(0, width), Labels(label_train))
 	krr.train(RealFeatures(fm_train))
 	out = krr.apply(RealFeatures(fm_test)).get_labels()
 
 	return krr,out
 
 if __name__=='__main__':
 	print 'KRR'
-	regression_krr_modular(*parameter_list[0])
+	regression_kernel_ridge_modular(*parameter_list[0])
diff --git a/examples/undocumented/python_modular/regression_least_squares_modular.py b/examples/undocumented/python_modular/regression_least_squares_modular.py
@@ -0,0 +1,29 @@
+###########################################################################
+# kernel ridge regression
+###########################################################################
+from numpy import array
+from numpy.random import seed, rand
+from tools.load import LoadMatrix
+lm=LoadMatrix()
+
+traindat = lm.load_numbers('../data/fm_train_real.dat')
+testdat = lm.load_numbers('../data/fm_test_real.dat')
+label_traindat = lm.load_labels('../data/label_train_twoclass.dat')
+
+
+parameter_list = [[traindat,testdat,label_traindat]]
+
+def regression_least_squares_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6):
+
+	from shogun.Features import Labels, RealFeatures
+	from shogun.Kernel import GaussianKernel
+	from shogun.Regression import LeastSquaresRegression
+
+	ls=LeastSquaresRegression(RealFeatures(traindat), Labels(label_train))
+	ls.train()
+	out = ls.apply(RealFeatures(fm_test)).get_labels()
+	return out,ls
+
+if __name__=='__main__':
+	print 'LeastSquaresRegression'
+	regression_least_squares_modular(*parameter_list[0])
diff --git a/examples/undocumented/python_modular/regression_linear_ridge_modular.py b/examples/undocumented/python_modular/regression_linear_ridge_modular.py
@@ -0,0 +1,29 @@
+###########################################################################
+# linear ridge regression
+###########################################################################
+from numpy import array
+from numpy.random import seed, rand
+from tools.load import LoadMatrix
+lm=LoadMatrix()
+
+traindat = lm.load_numbers('../data/fm_train_real.dat')
+testdat = lm.load_numbers('../data/fm_test_real.dat')
+label_traindat = lm.load_labels('../data/label_train_twoclass.dat')
+
+
+parameter_list = [[traindat,testdat,label_traindat,1e-6],[traindat,testdat,label_traindat,100]]
+
+def regression_linear_ridge_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6):
+
+	from shogun.Features import Labels, RealFeatures
+	from shogun.Kernel import GaussianKernel
+	from shogun.Regression import LinearRidgeRegression
+
+	rr=LinearRidgeRegression(tau, RealFeatures(traindat), Labels(label_train))
+	rr.train()
+	out = rr.apply(RealFeatures(fm_test)).get_labels()
+	return out,rr
+
+if __name__=='__main__':
+	print 'LinearRidgeRegression'
+	regression_linear_ridge_modular(*parameter_list[0])
diff --git a/examples/undocumented/ruby_modular/regression_krr_modular.rb b/examples/undocumented/ruby_modular/regression_krr_modular.rb
@@ -24,7 +24,7 @@ def regression_krr_modular(fm_train=traindat,fm_test=testdat,label_train=label_t
 
 	labels=Modshogun::Labels.new(label_train)
 
-	krr=Modshogun::KRR.new(tau, kernel, labels)
+	krr=Modshogun::KernelRidgeRegression.new(tau, kernel, labels)
 	krr.train(feats_train)
 
 	kernel.init(feats_train, feats_test)
@@ -39,8 +39,8 @@ def krr_short()
 	puts 'KRR_short'
 
 	width=0.8; tau=1e-6
-# *** 	krr=KRR(tau, GaussianKernel(0, width), Labels(label_train))
-	krr=Modshogun::KRR.new(tau, GaussianKernel(0, width), Labels(label_train))
+# *** 	krr=KernelRidgeRegression(tau, GaussianKernel(0, width), Labels(label_train))
+	krr=Modshogun::KernelRidgeRegression.new(tau, GaussianKernel(0, width), Labels(label_train))
 	#krr.set_features(tau, GaussianKernel(0, width), Labels(label_train))
 	krr.train(RealFeatures(fm_train))
 	out = krr.apply(RealFeatures(fm_test)).get_labels()
@@ -50,6 +50,6 @@ def krr_short()
 end
 
 if __FILE__ == $0
-	puts 'KRR'
+	puts 'KernelRidgeRegression'
 	pp regression_krr_modular(*parameter_list[0])
 end
diff --git a/src/interfaces/modular/Regression.i b/src/interfaces/modular/Regression.i
@@ -10,7 +10,9 @@
 
 /* Remove C Prefix */
 %rename(Regression) CRegression;
-%rename(KRR) CKRR;
+%rename(KernelRidgeRegression) CKernelRidgeRegression;
+%rename(LinearRidgeRegression) CLinearRidgeRegression;
+%rename(LeastSquaresRegression) CLeastSquaresRegression;
 %rename(LibSVR) CLibSVR;
 %rename(MKL) CMKL;
 %rename(MKLRegression) CMKLRegression;
@@ -20,7 +22,9 @@
 
 /* Include Class Headers to make them visible from within the target language */
 %include <shogun/regression/Regression.h>
-%include <shogun/regression/KRR.h>
+%include <shogun/regression/KernelRidgeRegression.h>
+%include <shogun/regression/LinearRidgeRegression.h>
+%include <shogun/regression/LeastSquaresRegression.h>
 %include <shogun/regression/svr/LibSVR.h>
 %include <shogun/classifier/mkl/MKL.h>
 %include <shogun/regression/svr/MKLRegression.h>

diff --git a/src/interfaces/modular/Regression_includes.i b/src/interfaces/modular/Regression_includes.i
@@ -2,7 +2,9 @@
  #include <shogun/regression/Regression.h>
  #include <shogun/machine/Machine.h>
  #include <shogun/machine/KernelMachine.h>
- #include <shogun/regression/KRR.h>
+ #include <shogun/regression/KernelRidgeRegression.h>
+ #include <shogun/regression/LinearRidgeRegression.h>
+ #include <shogun/regression/LeastSquaresRegression.h>
  #include <shogun/classifier/svm/SVM.h>
  #include <shogun/classifier/svm/LibSVM.h>
  #include <shogun/regression/svr/LibSVR.h>

diff --git a/src/shogun/machine/Machine.h b/src/shogun/machine/Machine.h
@@ -43,7 +43,7 @@ enum EClassifierType
 	CT_LPBOOST = 120,
 	CT_KNN = 130,
 	CT_SVMLIN=140,
-	CT_KRR = 150,
+	CT_KERNELRIDGEREGRESSION = 150,
 	CT_GNPPSVM = 160,
 	CT_GMNPSVM = 170,
 	CT_SUBGRADIENTSVM = 180,
@@ -68,7 +68,9 @@ enum EClassifierType
 	CT_GAUSSIANNAIVEBAYES = 370,
 	CT_AVERAGEDPERCEPTRON = 380,
 	CT_SGDQN = 390,
-	CT_CONJUGATEINDEX = 400
+	CT_CONJUGATEINDEX = 400,
+	CT_LINEARRIDGEREGRESSION = 410,
+	CT_LEASTSQUARESREGRESSION = 420
 };
 
 /** solver type */