Fallback for python tapkee examples

fresheye · Jan 18, 2013 · d3939f7 · d3939f7
1 parent 3d866b1
commit d3939f7
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Showing 11 changed files with 153 additions and 128 deletions.
diff --git a/examples/undocumented/python_modular/converter_diffusionmaps_modular.py b/examples/undocumented/python_modular/converter_diffusionmaps_modular.py
@@ -7,20 +7,22 @@
 parameter_list = [[data,10],[data,20]]
 
 def converter_diffusionmaps_modular (data,t):
-	from shogun.Features import RealFeatures
-	from shogun.Converter import DiffusionMaps
-	from shogun.Kernel import GaussianKernel
-
-	features = RealFeatures(data)
+	try: 
+		from shogun.Features import RealFeatures
+		from shogun.Converter import DiffusionMaps
+		from shogun.Kernel import GaussianKernel
 
-	converter = DiffusionMaps()
-	converter.set_target_dim(1)
-	converter.set_kernel(GaussianKernel(10,10.0))
-	converter.set_t(t)
-	converter.apply(features)
-
-	return features
+		features = RealFeatures(data)
+			
+		converter = DiffusionMaps()
+		converter.set_target_dim(1)
+		converter.set_kernel(GaussianKernel(10,10.0))
+		converter.set_t(t)
+		converter.apply(features)
 
+		return features
+	except ImportError:
+		print('No Eigen3 available')
 
 if __name__=='__main__':
 	print('DiffusionMaps')

diff --git a/examples/undocumented/python_modular/converter_hessianlocallylinearembedding_modular.py b/examples/undocumented/python_modular/converter_hessianlocallylinearembedding_modular.py
@@ -7,18 +7,20 @@
 parameter_list = [[data,20],[data,30]]
 
 def converter_hessianlocallylinearembedding_modular (data,k):
-	from shogun.Features import RealFeatures
-	from shogun.Converter import HessianLocallyLinearEmbedding
-
-	features = RealFeatures(data)
+	try:
+		from shogun.Features import RealFeatures
+		from shogun.Converter import HessianLocallyLinearEmbedding
 
-	converter = HessianLocallyLinearEmbedding()
-	converter.set_target_dim(1)
-	converter.set_k(k)
-	converter.apply(features)
-
-	return features
+		features = RealFeatures(data)
+			
+		converter = HessianLocallyLinearEmbedding()
+		converter.set_target_dim(1)
+		converter.set_k(k)
+		converter.apply(features)
 
+		return features
+	except ImportError:
+		print('No Eigen3 available')
 
 if __name__=='__main__':
 	print('HessianLocallyLinearEmbedding')

diff --git a/examples/undocumented/python_modular/converter_isomap_modular.py b/examples/undocumented/python_modular/converter_isomap_modular.py
@@ -7,18 +7,20 @@
 parameter_list = [[data]]
 
 def converter_isomap_modular (data):
-	from shogun.Features import RealFeatures
-	from shogun.Converter import Isomap
-
-	features = RealFeatures(data)
+	try:
+		from shogun.Features import RealFeatures
+		from shogun.Converter import Isomap
 
-	converter = Isomap()
-	converter.set_k(20)
-	converter.set_target_dim(1)
-	converter.apply(features)
-
-	return features
+		features = RealFeatures(data)
+			
+		converter = Isomap()
+		converter.set_k(20)
+		converter.set_target_dim(1)
+		converter.apply(features)
 
+		return features
+	except ImportError:
+		print('No Eigen3 available')
 
 if __name__=='__main__':
 	print('Isomap')

diff --git a/examples/undocumented/python_modular/converter_kernellocallylinearembedding_modular.py b/examples/undocumented/python_modular/converter_kernellocallylinearembedding_modular.py
@@ -7,21 +7,23 @@
 parameter_list = [[data,20],[data,30]]
 
 def converter_kernellocallylinearembedding_modular (data,k):
-	from shogun.Features import RealFeatures
-	from shogun.Converter import KernelLocallyLinearEmbedding
-	from shogun.Kernel import LinearKernel
-
-	features = RealFeatures(data)
+	try:
+		from shogun.Features import RealFeatures
+		from shogun.Converter import KernelLocallyLinearEmbedding
+		from shogun.Kernel import LinearKernel
 
-	kernel = LinearKernel()
-
-	converter = KernelLocallyLinearEmbedding(kernel)
-	converter.set_target_dim(1)
-	converter.set_k(k)
-	converter.apply(features)
-
-	return features
-
+		features = RealFeatures(data)
+
+		kernel = LinearKernel()
+
+		converter = KernelLocallyLinearEmbedding(kernel)
+		converter.set_target_dim(1)
+		converter.set_k(k)
+		converter.apply(features)
+
+		return features
+	except ImportError:
+		print('No Eigen3 available')
 
 if __name__=='__main__':
 	print('KernelLocallyLinearEmbedding')

diff --git a/examples/undocumented/python_modular/converter_laplacianeigenmaps_modular.py b/examples/undocumented/python_modular/converter_laplacianeigenmaps_modular.py
@@ -7,19 +7,21 @@
 parameter_list = [[data,20],[data,30]]
 
 def converter_laplacianeigenmaps_modular (data,k):
-	from shogun.Features import RealFeatures
-	from shogun.Converter import LaplacianEigenmaps
-
-	features = RealFeatures(data)
+	try:
+		from shogun.Features import RealFeatures
+		from shogun.Converter import LaplacianEigenmaps
 
-	converter = LaplacianEigenmaps()
-	converter.set_target_dim(1)
-	converter.set_k(k)
-	converter.set_tau(2.0)
-	converter.apply(features)
-
-	return features
+		features = RealFeatures(data)
+			
+		converter = LaplacianEigenmaps()
+		converter.set_target_dim(1)
+		converter.set_k(k)
+		converter.set_tau(2.0)
+		converter.apply(features)
 
+		return features
+	except ImportError:
+		print('No Eigen3 available')
 
 if __name__=='__main__':
 	print('LaplacianEigenmaps')

diff --git a/examples/undocumented/python_modular/converter_linearlocaltangentspacealignment_modular.py b/examples/undocumented/python_modular/converter_linearlocaltangentspacealignment_modular.py
@@ -7,18 +7,20 @@
 parameter_list = [[data,20],[data,30]]
 
 def converter_linearlocaltangentspacealignment_modular (data,k):
-	from shogun.Features import RealFeatures
-	from shogun.Converter import LinearLocalTangentSpaceAlignment
-
-	features = RealFeatures(data)
+	try:
+		from shogun.Features import RealFeatures
+		from shogun.Converter import LinearLocalTangentSpaceAlignment
 
-	converter = LinearLocalTangentSpaceAlignment()
-	converter.set_target_dim(1)
-	converter.set_k(k)
-	converter.apply(features)
-
-	return features
+		features = RealFeatures(data)
+			
+		converter = LinearLocalTangentSpaceAlignment()
+		converter.set_target_dim(1)
+		converter.set_k(k)
+		converter.apply(features)
 
+		return features
+	except ImportError:
+		print('No Eigen3 available')
 
 if __name__=='__main__':
 	print('LinearLocalTangentSpaceAlignment')

diff --git a/examples/undocumented/python_modular/converter_localitypreservingprojections_modular.py b/examples/undocumented/python_modular/converter_localitypreservingprojections_modular.py
@@ -7,18 +7,20 @@
 parameter_list = [[data,20],[data,30]]
 
 def converter_localitypreservingprojections_modular (data,k):
-	from shogun.Features import RealFeatures
-	from shogun.Converter import LocalityPreservingProjections
-
-	features = RealFeatures(data)
-	converter = LocalityPreservingProjections()
-	converter.set_target_dim(1)
-	converter.set_k(k)
-	converter.set_tau(2.0)
-	converter.apply(features)
-
-	return features
+	try:
+		from shogun.Features import RealFeatures
+		from shogun.Converter import LocalityPreservingProjections
+
+		features = RealFeatures(data)
+		converter = LocalityPreservingProjections()
+		converter.set_target_dim(1)
+		converter.set_k(k)
+		converter.set_tau(2.0)
+		converter.apply(features)
 
+		return features
+	except ImportError:
+		print('No Eigen3 available')
 
 if __name__=='__main__':
 	print('LocalityPreservingProjections')

diff --git a/examples/undocumented/python_modular/converter_locallylinearembedding_modular.py b/examples/undocumented/python_modular/converter_locallylinearembedding_modular.py
@@ -7,18 +7,20 @@
 parameter_list = [[data,20],[data,30]]
 
 def converter_locallylinearembedding_modular (data,k):
-	from shogun.Features import RealFeatures
-	from shogun.Converter import LocallyLinearEmbedding
-
-	features = RealFeatures(data)
+	try:
+		from shogun.Features import RealFeatures
+		from shogun.Converter import LocallyLinearEmbedding
 
-	converter = LocallyLinearEmbedding()
-	converter.set_target_dim(1)
-	converter.set_k(k)
-	converter.apply(features)
-
-	return features
+		features = RealFeatures(data)
+			
+		converter = LocallyLinearEmbedding()
+		converter.set_target_dim(1)
+		converter.set_k(k)
+		converter.apply(features)
 
+		return features
+	except ImportError:
+		print('No Eigen3 available')
 
 if __name__=='__main__':
 	print('LocallyLinearEmbedding')

diff --git a/examples/undocumented/python_modular/converter_localtangentspacealignment_modular.py b/examples/undocumented/python_modular/converter_localtangentspacealignment_modular.py
@@ -7,17 +7,20 @@
 parameter_list = [[data,20],[data,30]]
 
 def converter_localtangentspacealignment_modular (data,k):
-	from shogun.Features import RealFeatures
-	from shogun.Converter import LocalTangentSpaceAlignment
-
-	features = RealFeatures(data)
+	try:
+		from shogun.Features import RealFeatures
+		from shogun.Converter import LocalTangentSpaceAlignment
 
-	converter = LocalTangentSpaceAlignment()
-	converter.set_target_dim(1)
-	converter.set_k(k)
-	converter.apply(features)
+		features = RealFeatures(data)
+
+		converter = LocalTangentSpaceAlignment()
+		converter.set_target_dim(1)
+		converter.set_k(k)
+		converter.apply(features)
 
-	return features
+		return features
+	except ImportError:
+		print('No Eigen3 available')
 
 
 if __name__=='__main__':

diff --git a/examples/undocumented/python_modular/converter_multidimensionalscaling_modular.py b/examples/undocumented/python_modular/converter_multidimensionalscaling_modular.py
@@ -8,27 +8,30 @@
 parameter_list = [[data]]
 
 def converter_multidimensionalscaling_modular (data):
-	from shogun.Features import RealFeatures
-	from shogun.Converter import MultidimensionalScaling
-	from shogun.Distance import EuclideanDistance
-
-	features = RealFeatures(data)
+	try:
+		from shogun.Features import RealFeatures
+		from shogun.Converter import MultidimensionalScaling
+		from shogun.Distance import EuclideanDistance
 
-	distance_before = EuclideanDistance()
-	distance_before.init(features,features)
+		features = RealFeatures(data)
+
+		distance_before = EuclideanDistance()
+		distance_before.init(features,features)
 
-	converter = MultidimensionalScaling()
-	converter.set_target_dim(2)
-	converter.set_landmark(False)
-	embedding =converter.apply(features)
+		converter = MultidimensionalScaling()
+		converter.set_target_dim(2)
+		converter.set_landmark(False)
+		embedding =converter.apply(features)
 
-	distance_after = EuclideanDistance()
-	distance_after.init(embedding,embedding)
+		distance_after = EuclideanDistance()
+		distance_after.init(embedding,embedding)
 
-	distance_matrix_after = distance_after.get_distance_matrix()
-	distance_matrix_before = distance_before.get_distance_matrix()
+		distance_matrix_after = distance_after.get_distance_matrix()
+		distance_matrix_before = distance_before.get_distance_matrix()
 
-	return numpy.linalg.norm(distance_matrix_after-distance_matrix_before)/numpy.linalg.norm(distance_matrix_before)
+		return numpy.linalg.norm(distance_matrix_after-distance_matrix_before)/numpy.linalg.norm(distance_matrix_before)
+	except ImportError:
+		print('No Eigen3 available')
 
 if __name__=='__main__':
 	print('MultidimensionalScaling')

diff --git a/examples/undocumented/python_modular/converter_stochasticproximityembedding_modular.py b/examples/undocumented/python_modular/converter_stochasticproximityembedding_modular.py
@@ -7,23 +7,26 @@
 parameter_list = [[data, 20]]
 
 def converter_stochasticproximityembedding_modular (data, k):
-	from shogun.Features import RealFeatures
-	from shogun.Converter import StochasticProximityEmbedding, SPE_GLOBAL, SPE_LOCAL
-
-	features = RealFeatures(data)
+	try:
+		from shogun.Features import RealFeatures
+		from shogun.Converter import StochasticProximityEmbedding, SPE_GLOBAL, SPE_LOCAL
 
-	converter = StochasticProximityEmbedding()
-	converter.set_target_dim(1)
-	converter.set_nupdates(40)
-	# Embed with local strategy
-	converter.set_k(k)
-	converter.set_strategy(SPE_LOCAL)
-	converter.embed(features)
-	# Embed with global strategy
-	converter.set_strategy(SPE_GLOBAL)
-	converter.embed(features)
+		features = RealFeatures(data)
+
+		converter = StochasticProximityEmbedding()
+		converter.set_target_dim(1)
+		converter.set_nupdates(40)
+		# Embed with local strategy
+		converter.set_k(k)
+		converter.set_strategy(SPE_LOCAL)
+		converter.embed(features)
+		# Embed with global strategy
+		converter.set_strategy(SPE_GLOBAL)
+		converter.embed(features)
 
-	return features
+		return features
+	except ImportError:
+		print('No Eigen3 available')
 
 if __name__=='__main__':
 	print('StochasticProximityEmbedding')