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fix various python examples
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Soeren Sonnenburg committed May 21, 2012
1 parent 1a6ae98 commit 5f9d58b
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Showing 11 changed files with 23 additions and 23 deletions.
4 changes: 2 additions & 2 deletions examples/undocumented/python_modular/classifier_averaged_perceptron_modular.py
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Expand Up @@ -8,13 +8,13 @@
parameter_list = [[traindat,testdat,label_traindat,1.,1000,1],[traindat,testdat,label_traindat,1.,1000,1]]

def classifier_averaged_perceptron_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,learn_rate=1.,max_iter=1000,num_threads=1):
from shogun.Features import RealFeatures, Labels
from shogun.Features import RealFeatures, BinaryLabels
from shogun.Classifier import AveragedPerceptron

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)

labels=Labels(label_train_twoclass)
labels=BinaryLabels(label_train_twoclass)

perceptron=AveragedPerceptron(feats_train, labels)
perceptron.set_learn_rate(learn_rate)
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4 changes: 2 additions & 2 deletions examples/undocumented/python_modular/classifier_conjugateindex_modular.py
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Expand Up @@ -8,13 +8,13 @@
parameter_list = [[traindat,testdat,label_traindat],[traindat,testdat,label_traindat]]

def classifier_conjugateindex_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat):
from shogun.Features import RealFeatures, Labels
from shogun.Features import RealFeatures, MulticlassLabels
from shogun.Classifier import ConjugateIndex

feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)

labels = Labels(label_train_multiclass)
labels = MulticlassLabels(label_train_multiclass)

ci = ConjugateIndex(feats_train, labels)
ci.train()
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4 changes: 2 additions & 2 deletions examples/undocumented/python_modular/classifier_custom_kernel_modular.py
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@@ -1,7 +1,7 @@
parameter_list = [[1,7],[2,8]]

def classifier_custom_kernel_modular(C=1,dim=7):
from shogun.Features import RealFeatures, Labels
from shogun.Features import RealFeatures, BinaryLabels
from shogun.Kernel import CustomKernel
from shogun.Classifier import LibSVM

Expand All @@ -16,7 +16,7 @@ def classifier_custom_kernel_modular(C=1,dim=7):

kernel=CustomKernel()
kernel.set_full_kernel_matrix_from_full(data)
labels=Labels(lab)
labels=BinaryLabels(lab)
svm=LibSVM(C, kernel, labels)
svm.train()
predictions =svm.apply()
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6 changes: 3 additions & 3 deletions examples/undocumented/python_modular/classifier_domainadaptationsvm_modular.py
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@@ -1,6 +1,6 @@
import numpy

from shogun.Features import StringCharFeatures, Labels, DNA
from shogun.Features import StringCharFeatures, BinaryLabels, DNA
from shogun.Kernel import WeightedDegreeStringKernel
from shogun.Classifier import SVMLight, DomainAdaptationSVM, MSG_DEBUG

Expand Down Expand Up @@ -66,7 +66,7 @@ def classifier_domainadaptationsvm_modular(fm_train_dna=traindna,fm_test_dna=tes
feats_train = StringCharFeatures(fm_train_dna, DNA)
feats_test = StringCharFeatures(fm_test_dna, DNA)
kernel = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels = Labels(label_train_dna)
labels = BinaryLabels(label_train_dna)
svm = SVMLight(C, kernel, labels)
svm.train()
#svm.io.set_loglevel(MSG_DEBUG)
Expand All @@ -78,7 +78,7 @@ def classifier_domainadaptationsvm_modular(fm_train_dna=traindna,fm_test_dna=tes
feats_train2 = StringCharFeatures(fm_train_dna, DNA)
feats_test2 = StringCharFeatures(fm_test_dna, DNA)
kernel2 = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels2 = Labels(label_train_dna)
labels2 = BinaryLabels(label_train_dna)

# we regularize against the previously obtained solution
dasvm = DomainAdaptationSVM(C, kernel2, labels2, svm, 1.0)
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4 changes: 2 additions & 2 deletions examples/undocumented/python_modular/classifier_gaussiannaivebayes_modular.py
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Expand Up @@ -7,12 +7,12 @@
parameter_list = [[traindat,testdat,label_traindat]]

def classifier_gaussiannaivebayes_modular(fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat):
from shogun.Features import RealFeatures, Labels
from shogun.Features import RealFeatures, MulticlassLabels
from shogun.Classifier import GaussianNaiveBayes

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
labels=Labels(label_train_multiclass)
labels=MulticlassLabels(label_train_multiclass)

gnb=GaussianNaiveBayes(feats_train, labels)
gnb_train = gnb.train()
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4 changes: 2 additions & 2 deletions examples/undocumented/python_modular/classifier_gmnpsvm_modular.py
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Expand Up @@ -9,7 +9,7 @@

def classifier_gmnpsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):

from shogun.Features import RealFeatures, Labels
from shogun.Features import RealFeatures, MulticlassLabels
from shogun.Kernel import GaussianKernel
from shogun.Classifier import GMNPSVM

Expand All @@ -18,7 +18,7 @@ def classifier_gmnpsvm_modular (fm_train_real=traindat,fm_test_real=testdat,labe

kernel=GaussianKernel(feats_train, feats_train, width)

labels=Labels(label_train_multiclass)
labels=MulticlassLabels(label_train_multiclass)

svm=GMNPSVM(C, kernel, labels)
svm.set_epsilon(epsilon)
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4 changes: 2 additions & 2 deletions examples/undocumented/python_modular/classifier_gpbtsvm_modular.py
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Expand Up @@ -10,14 +10,14 @@
def classifier_gpbtsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,width=2.1,C=1,epsilon=1e-5):


from shogun.Features import RealFeatures, Labels
from shogun.Features import RealFeatures, BinaryLabels
from shogun.Kernel import GaussianKernel
from shogun.Classifier import GPBTSVM

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
kernel=GaussianKernel(feats_train, feats_train, width)
labels=Labels(label_train_twoclass)
labels=BinaryLabels(label_train_twoclass)

svm=GPBTSVM(C, kernel, labels)
svm.set_epsilon(epsilon)
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4 changes: 2 additions & 2 deletions examples/undocumented/python_modular/classifier_knn_modular.py
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Expand Up @@ -7,7 +7,7 @@
parameter_list = [[traindat,testdat,label_traindat,3],[traindat,testdat,label_traindat,3]]

def classifier_knn_modular(fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat, k=3 ):
from shogun.Features import RealFeatures, Labels
from shogun.Features import RealFeatures, MulticlassLabels
from shogun.Classifier import KNN
from shogun.Distance import EuclidianDistance

Expand All @@ -16,7 +16,7 @@ def classifier_knn_modular(fm_train_real=traindat,fm_test_real=testdat,label_tra
distance=EuclidianDistance(feats_train, feats_train)


labels=Labels(label_train_multiclass)
labels=MulticlassLabels(label_train_multiclass)

knn=KNN(k, distance, labels)
knn_train = knn.train()
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4 changes: 2 additions & 2 deletions examples/undocumented/python_modular/classifier_larank_modular.py
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Expand Up @@ -8,7 +8,7 @@
parameter_list = [[traindat,testdat,label_traindat,0.9,1,2000],[traindat,testdat,label_traindat,3,1,5000]]

def classifier_larank_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,C=0.9,num_threads=1,num_iter=5):
from shogun.Features import RealFeatures, Labels
from shogun.Features import RealFeatures, MulticlassLabels
from shogun.Kernel import GaussianKernel
from shogun.Classifier import LaRank
from shogun.Mathematics import Math_init_random
Expand All @@ -20,7 +20,7 @@ def classifier_larank_modular (fm_train_real=traindat,fm_test_real=testdat,label
kernel=GaussianKernel(feats_train, feats_train, width)

epsilon=1e-5
labels=Labels(label_train_multiclass)
labels=MulticlassLabels(label_train_multiclass)

svm=LaRank(C, kernel, labels)
#svm.set_tau(1e-3)
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4 changes: 2 additions & 2 deletions examples/undocumented/python_modular/classifier_lda_modular.py
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Expand Up @@ -8,13 +8,13 @@
parameter_list = [[traindat,testdat,label_traindat,3,1],[traindat,testdat,label_traindat,4,1]]

def classifier_lda_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,gamma=3,num_threads=1):
from shogun.Features import RealFeatures, Labels
from shogun.Features import RealFeatures, BinaryLabels
from shogun.Classifier import LDA

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)

labels=Labels(label_train_twoclass)
labels=BinaryLabels(label_train_twoclass)

lda=LDA(gamma, feats_train, labels)
lda.train()
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4 changes: 2 additions & 2 deletions examples/undocumented/python_modular/classifier_liblinear_modular.py
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Expand Up @@ -10,14 +10,14 @@
def classifier_liblinear_modular(fm_train_real, fm_test_real,
label_train_twoclass, C, epsilon):

from shogun.Features import RealFeatures, SparseRealFeatures, Labels
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=Labels(label_train_twoclass)
labels=BinaryLabels(label_train_twoclass)

svm=LibLinear(C, feats_train, labels)
svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL)
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