added example for DirectorDotFeatures

shogun-toolbox · Aug 25, 2012 · ab15b5f · ab15b5f
1 parent d30bf9e
commit ab15b5f
Showing 1 changed file with 108 additions and 0 deletions.
diff --git a/examples/undocumented/python_modular/features_director_dot_modular.py b/examples/undocumented/python_modular/features_director_dot_modular.py
@@ -0,0 +1,108 @@
+import numpy
+try:
+	from shogun.Features import DirectorDotFeatures
+	from shogun.Library import RealVector
+except ImportError:
+	print "recompile shogun with --enable-swig-directors"
+	import sys
+	sys.exit(0)
+
+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,0.9,1e-3],[traindat,testdat,label_traindat,0.8,1e-2]]
+
+class NumpyFeatures(DirectorDotFeatures):
+
+	# variables
+	data=numpy.empty((1,1))
+
+	# constructor
+	def __init__(self, d):
+		DirectorDotFeatures.__init__(self)
+		self.data = d
+
+	# overloaded methods
+	def add_to_dense_sgvec(self, alpha, vec_idx1, vec2, abs):
+		if abs:
+			vec2+=alpha*numpy.abs(self.data[:,vec_idx1])
+		else:
+			vec2+=alpha*self.data[:,vec_idx1]
+
+	def dot(self, vec_idx1, df, vec_idx2):
+		return numpy.dot(self.data[:,vec_idx1], df.get_computed_dot_feature_vector(vec_idx2))
+
+	def dense_dot_sgvec(self, vec_idx1, vec2):
+		return numpy.dot(self.data[:,vec_idx1], vec2[0:vec2.vlen])
+
+	def get_num_vectors(self):
+		return self.data.shape[1]
+
+	def get_dim_feature_space(self):
+		return self.data.shape[0]
+
+	# operators
+#	def __add__(self, other):
+#		return NumpyFeatures(self.data+other.data)
+
+#	def __sub__(self, other):
+#		return NumpyFeatures(self.data-other.data)
+
+#	def __iadd__(self, other):
+#		return NumpyFeatures(self.data+other.data)
+
+#	def __isub__(self, other):
+#		return NumpyFeatures(self.data-other.data)
+
+def features_director_dot_modular (fm_train_real, fm_test_real,
+		label_train_twoclass, C, epsilon):
+
+	from shogun.Features import RealFeatures, SparseRealFeatures, BinaryLabels
+	from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC_DUAL
+	from shogun.Mathematics import Math_init_random
+	Math_init_random(17)
+
+	feats_train=RealFeatures(fm_train_real)
+	feats_test=RealFeatures(fm_test_real)
+	labels=BinaryLabels(label_train_twoclass)
+
+	dfeats_train=NumpyFeatures(fm_train_real)
+	dfeats_test=NumpyFeatures(fm_test_real)
+	dlabels=BinaryLabels(label_train_twoclass)
+
+	print feats_train.get_computed_dot_feature_matrix()
+	print dfeats_train.get_computed_dot_feature_matrix()
+
+	svm=LibLinear(C, feats_train, labels)
+	svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL)
+	svm.set_epsilon(epsilon)
+	svm.set_bias_enabled(True)
+	svm.train()
+
+	svm.set_features(feats_test)
+	svm.apply().get_labels()
+	predictions = svm.apply()
+
+	dfeats_train.__disown__()
+	dfeats_train.parallel.set_num_threads(1)
+	dsvm=LibLinear(C, dfeats_train, dlabels)
+	dsvm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL)
+	dsvm.set_epsilon(epsilon)
+	dsvm.set_bias_enabled(True)
+	dsvm.train()
+
+	dfeats_test.__disown__()
+	dfeats_test.parallel.set_num_threads(1)
+	dsvm.set_features(dfeats_test)
+	dsvm.apply().get_labels()
+	dpredictions = dsvm.apply()
+
+	return predictions, svm, predictions.get_labels()
+
+if __name__=='__main__':
+	print('DirectorLinear')
+	features_director_dot_modular(*parameter_list[0])