Python RealFeatures.get_num_vectors方法代码示例

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
def feature_function():
    
    from modshogun import RealFeatures
    from modshogun import CSVFile
    import numpy as np

    #3x3 random matrix 
    feat_arr = np.random.rand(3, 3)
    
    #initialize RealFeatures from numpy array
    features = RealFeatures(feat_arr)

    #get matrix value function
    print features.get_feature_matrix(features)
    
    #get selected column of matrix
    print features.get_feature_vector(1)

    #get number of columns
    print features.get_num_features()

    #get number of rows
    print features.get_num_vectors()
    
    feats_from_csv = RealFeatures(CSVFile("csv/feature.csv"))
    print "csv is ", feats_from_csv.get_feature_matrix()

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
def transfer_multitask_leastsquares_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
	from modshogun import RegressionLabels, RealFeatures, Task, TaskGroup
	try:
		from modshogun import MultitaskLeastSquaresRegression
	except ImportError:
		print("MultitaskLeastSquaresRegression not available")
		exit(0)

	features = RealFeatures(traindat)
	labels = RegressionLabels(label_train)

	n_vectors = features.get_num_vectors()
	task_one = Task(0,n_vectors//2)
	task_two = Task(n_vectors//2,n_vectors)
	task_group = TaskGroup()
	task_group.append_task(task_one)
	task_group.append_task(task_two)

	mtlsr = MultitaskLeastSquaresRegression(0.1,features,labels,task_group)
	mtlsr.set_regularization(1) # use regularization ratio
	mtlsr.set_tolerance(1e-2) # use 1e-2 tolerance
	mtlsr.train()
	mtlsr.set_current_task(0)
	out = mtlsr.apply_regression().get_labels()
	return out

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
def transfer_multitask_clustered_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
	from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup, MSG_DEBUG
	try:
		from modshogun import MultitaskClusteredLogisticRegression
	except ImportError:
		print("MultitaskClusteredLogisticRegression not available")
		exit()

	features = RealFeatures(hstack((traindat,sin(traindat),cos(traindat))))
	labels = BinaryLabels(hstack((label_train,label_train,label_train)))

	n_vectors = features.get_num_vectors()
	task_one = Task(0,n_vectors//3)
	task_two = Task(n_vectors//3,2*n_vectors//3)
	task_three = Task(2*n_vectors//3,n_vectors)
	task_group = TaskGroup()
	task_group.append_task(task_one)
	task_group.append_task(task_two)
	task_group.append_task(task_three)

	mtlr = MultitaskClusteredLogisticRegression(1.0,100.0,features,labels,task_group,2)
	#mtlr.io.set_loglevel(MSG_DEBUG)
	mtlr.set_tolerance(1e-3) # use 1e-2 tolerance
	mtlr.set_max_iter(100)
	mtlr.train()
	mtlr.set_current_task(0)
	#print mtlr.get_w()
	out = mtlr.apply_regression().get_labels()

	return out

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
def transfer_multitask_l12_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):
	from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup
	try:
		from modshogun import MultitaskL12LogisticRegression
	except ImportError:
		print("MultitaskL12LogisticRegression not available")
		exit(0)

	features = RealFeatures(hstack((traindat,traindat)))
	labels = BinaryLabels(hstack((label_train,label_train)))

	n_vectors = features.get_num_vectors()
	task_one = Task(0,n_vectors//2)
	task_two = Task(n_vectors//2,n_vectors)
	task_group = TaskGroup()
	task_group.append_task(task_one)
	task_group.append_task(task_two)

	mtlr = MultitaskL12LogisticRegression(0.1,0.1,features,labels,task_group)
	mtlr.set_tolerance(1e-2) # use 1e-2 tolerance
	mtlr.set_max_iter(10)
	mtlr.train()
	mtlr.set_current_task(0)
	out = mtlr.apply_regression().get_labels()

	return out

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
def load_data(num_train_samples=7291, m_data_dict=data_dict):
	from modshogun import RealFeatures, MulticlassLabels
	import numpy

	train_vec = m_data_dict['yTr'][0][:num_train_samples].astype(numpy.float64)
	train_labels = MulticlassLabels(train_vec)
	test_vec = m_data_dict['yTe'][0].astype(numpy.float64)
 	test_labels = MulticlassLabels(test_vec)
	print "#train_labels = " + str(train_labels.get_num_labels())
	print "#test_labels  = " + str(test_labels.get_num_labels())

	train_mat = m_data_dict['xTr'][:,:num_train_samples].astype(numpy.float64)
	train_features = RealFeatures(train_mat)
	test_mat = m_data_dict['xTe'].astype(numpy.float64)
	test_features = RealFeatures(test_mat)
	print "#train_vectors = " + str(train_features.get_num_vectors())
	print "#test_vectors  = " + str(test_features.get_num_vectors())
	print "data dimension = " + str(test_features.get_num_features())

	return train_features, train_labels, test_features, test_labels

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
def multiclass_c45classifiertree_modular(train=traindat,test=testdat,labels=label_traindat,ft=feattypes):
	try:
		from modshogun import RealFeatures, MulticlassLabels, CSVFile, C45ClassifierTree
		from numpy import random, int32
	except ImportError:
		print("Could not import Shogun and/or numpy modules")
		return

	# wrap features and labels into Shogun objects
	feats_train=RealFeatures(CSVFile(train))
	feats_test=RealFeatures(CSVFile(test))
	train_labels=MulticlassLabels(CSVFile(labels))

	# divide train dataset into training and validation subsets in the ratio 2/3 to 1/3
	subset=int32(random.permutation(feats_train.get_num_vectors()))
	vsubset=subset[1:subset.size/3]
	trsubset=subset[1+subset.size/3:subset.size]

	# C4.5 Tree formation using training subset
	train_labels.add_subset(trsubset)
	feats_train.add_subset(trsubset)

	c=C45ClassifierTree()
	c.set_labels(train_labels)
	c.set_feature_types(ft)
	c.train(feats_train)

	train_labels.remove_subset()
	feats_train.remove_subset()

	# prune tree using validation subset
	train_labels.add_subset(vsubset)
	feats_train.add_subset(vsubset)

	c.prune_tree(feats_train,train_labels)

	train_labels.remove_subset()
	feats_train.remove_subset()

	# Classify test data
	output=c.apply_multiclass(feats_test).get_labels()
	output_certainty=c.get_certainty_vector()

	return c,output,output_certainty

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
def transfer_multitask_group_regression(fm_train=traindat,fm_test=testdat,label_train=label_traindat):

	from modshogun import RegressionLabels, RealFeatures, Task, TaskGroup, MultitaskLSRegression

	features = RealFeatures(traindat)
	labels = RegressionLabels(label_train)

	n_vectors = features.get_num_vectors()
	task_one = Task(0,n_vectors/2)
	task_two = Task(n_vectors/2,n_vectors)
	task_group = TaskGroup()
	task_group.add_task(task_one)
	task_group.add_task(task_two)

	mtlsr = MultitaskLSRegression(0.1,features,labels,task_group)
	mtlsr.train()
	mtlsr.set_current_task(0)
	out = mtlsr.apply_regression().get_labels()
	return out

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
def transfer_multitask_logistic_regression (fm_train=traindat,fm_test=testdat,label_train=label_traindat):

	from modshogun import BinaryLabels, RealFeatures, Task, TaskGroup, MultitaskLogisticRegression

	features = RealFeatures(hstack((traindat,traindat)))
	labels = BinaryLabels(hstack((label_train,label_train)))

	n_vectors = features.get_num_vectors()
	task_one = Task(0,n_vectors/2)
	task_two = Task(n_vectors/2,n_vectors)
	task_group = TaskGroup()
	task_group.append_task(task_one)
	task_group.append_task(task_two)

	mtlr = MultitaskLogisticRegression(0.1,features,labels,task_group)
	mtlr.set_regularization(1) # use regularization ratio
	mtlr.set_tolerance(1e-2) # use 1e-2 tolerance
	mtlr.train()
	mtlr.set_current_task(0)
	out = mtlr.apply().get_labels()

	return out

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
def metric_lmnn_statistics(
    k=3,
    fname_features="../../data/fm_train_multiclass_digits.dat.gz",
    fname_labels="../../data/label_train_multiclass_digits.dat",
):
    try:
        from modshogun import LMNN, CSVFile, RealFeatures, MulticlassLabels, MSG_DEBUG
        import matplotlib.pyplot as pyplot
    except ImportError:
        print "Error importing modshogun or other required modules. Please, verify their installation."
        return

    features = RealFeatures(load_compressed_features(fname_features).T)
    labels = MulticlassLabels(CSVFile(fname_labels))

    # 	print 'number of examples = %d' % features.get_num_vectors()
    # 	print 'number of features = %d' % features.get_num_features()

    assert features.get_num_vectors() == labels.get_num_labels()

    # train LMNN
    lmnn = LMNN(features, labels, k)
    lmnn.set_correction(100)
    # 	lmnn.io.set_loglevel(MSG_DEBUG)
    print "Training LMNN, this will take about two minutes..."
    lmnn.train()
    print "Training done!"

    # plot objective obtained during training
    statistics = lmnn.get_statistics()

    pyplot.plot(statistics.obj.get())
    pyplot.grid(True)
    pyplot.xlabel("Iterations")
    pyplot.ylabel("LMNN objective")
    pyplot.title("LMNN objective during training for the multiclass digits data set")

    pyplot.show()

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
    def train(self, images, labels):
        """
        Train eigenfaces
        """
        print "Train...",
        #copy labels
        self._labels = labels;

        #transform the numpe vector to shogun structure
        features = RealFeatures(images)
        #PCA
        self.pca = PCA()
        #set dimension
        self.pca.set_target_dim(self._num_components);
        #compute PCA
        self.pca.init(features)

        for sampleIdx in range(features.get_num_vectors()):
            v = features.get_feature_vector(sampleIdx);
            p = self.pca.apply_to_feature_vector(v);
            self._projections.insert(sampleIdx, p);

        print "ok!"

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
		xi = x[y==val]
		axis.scatter(xi[:,0], xi[:,1], s=50, facecolors='none', edgecolors=COLS[idx])

def plot_neighborhood_graph(x, nn, axis):
	for i in xrange(x.shape[0]):
		xs = [x[i,0], x[nn[1,i], 0]]
		ys = [x[i,1], x[nn[1,i], 1]]
		axis.plot(xs, ys, COLS[int(y[i])])

figure, axarr = pyplot.subplots(3, 1)
x, y = sandwich_data()

features = RealFeatures(x.T)
labels = MulticlassLabels(y)

print('%d vectors with %d features' % (features.get_num_vectors(), features.get_num_features()))
assert(features.get_num_vectors() == labels.get_num_labels())

distance = EuclideanDistance(features, features)
k = 2
knn = KNN(k, distance, labels)

plot_data(x, y, axarr[0])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[0])
axarr[0].set_aspect('equal')
axarr[0].set_xlim(-6, 4)
axarr[0].set_ylim(-3, 2)

lmnn = LMNN(features, labels, k)
lmnn.set_maxiter(10000)
lmnn.train()

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
def hsic_graphical():
	# parameters, change to get different results
	m=250
	difference=3

	# setting the angle lower makes a harder test
	angle=pi/30

	# number of samples taken from null and alternative distribution
	num_null_samples=500

	# use data generator class to produce example data
	data=DataGenerator.generate_sym_mix_gauss(m,difference,angle)

	# create shogun feature representation
	features_x=RealFeatures(array([data[0]]))
	features_y=RealFeatures(array([data[1]]))

	# compute median data distance in order to use for Gaussian kernel width
	# 0.5*median_distance normally (factor two in Gaussian kernel)
	# However, shoguns kernel width is different to usual parametrization
	# Therefore 0.5*2*median_distance^2
	# Use a subset of data for that, only 200 elements. Median is stable
	subset=int32(array([x for x in range(features_x.get_num_vectors())])) # numpy
	subset=random.permutation(subset) # numpy permutation
	subset=subset[0:200]
	features_x.add_subset(subset)
	dist=EuclideanDistance(features_x, features_x)
	distances=dist.get_distance_matrix()
	features_x.remove_subset()
	median_distance=np.median(distances)
	sigma_x=median_distance**2
	features_y.add_subset(subset)
	dist=EuclideanDistance(features_y, features_y)
	distances=dist.get_distance_matrix()
	features_y.remove_subset()
	median_distance=np.median(distances)
	sigma_y=median_distance**2
	print "median distance for Gaussian kernel on x:", sigma_x
	print "median distance for Gaussian kernel on y:", sigma_y
	kernel_x=GaussianKernel(10,sigma_x)
	kernel_y=GaussianKernel(10,sigma_y)

	# create hsic instance. Note that this is a convienience constructor which copies
	# feature data. features_x and features_y are not these used in hsic.
	# This is only for user-friendlyness. Usually, its ok to do this.
	# Below, the alternative distribution is sampled, which means
	# that new feature objects have to be created in each iteration (slow)
	# However, normally, the alternative distribution is not sampled
	hsic=HSIC(kernel_x,kernel_y,features_x,features_y)

	# sample alternative distribution
	alt_samples=zeros(num_null_samples)
	for i in range(len(alt_samples)):
		data=DataGenerator.generate_sym_mix_gauss(m,difference,angle)
		features_x.set_feature_matrix(array([data[0]]))
		features_y.set_feature_matrix(array([data[1]]))

		# re-create hsic instance everytime since feature objects are copied due to
		# useage of convienience constructor
		hsic=HSIC(kernel_x,kernel_y,features_x,features_y)
		alt_samples[i]=hsic.compute_statistic()

	# sample from null distribution
	# permutation, biased statistic
	hsic.set_null_approximation_method(PERMUTATION)
	hsic.set_num_null_samples(num_null_samples)
	null_samples_boot=hsic.sample_null()

	# fit gamma distribution, biased statistic
	hsic.set_null_approximation_method(HSIC_GAMMA)
	gamma_params=hsic.fit_null_gamma()
	# sample gamma with parameters
	null_samples_gamma=array([gamma(gamma_params[0], gamma_params[1]) for _ in range(num_null_samples)])

	# plot
	figure()

	# plot data x and y
	subplot(2,2,1)
	gca().xaxis.set_major_locator( MaxNLocator(nbins = 4) ) # reduce number of x-ticks
	gca().yaxis.set_major_locator( MaxNLocator(nbins = 4) ) # reduce number of x-ticks
	grid(True)
	plot(data[0], data[1], 'o')
	title('Data, rotation=$\pi$/'+str(1/angle*pi)+'\nm='+str(m))
	xlabel('$x$')
	ylabel('$y$')

	# compute threshold for test level
	alpha=0.05
	null_samples_boot.sort()
	null_samples_gamma.sort()
	thresh_boot=null_samples_boot[floor(len(null_samples_boot)*(1-alpha))];
	thresh_gamma=null_samples_gamma[floor(len(null_samples_gamma)*(1-alpha))];

	type_one_error_boot=sum(null_samples_boot<thresh_boot)/float(num_null_samples)
	type_one_error_gamma=sum(null_samples_gamma<thresh_boot)/float(num_null_samples)

	# plot alternative distribution with threshold
	subplot(2,2,2)
#.........这里部分代码省略.........

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
#!/usr/bin/python

from modshogun import CSVFile, RealFeatures, RescaleFeatures
from scipy.linalg import solve_triangular, cholesky, sqrtm, inv
import matplotlib.pyplot as pyplot
import numpy

# load wine features
features = RealFeatures(CSVFile('../data/fm_wine.dat'))

print('%d vectors with %d features.' % (features.get_num_vectors(), features.get_num_features()))
print('original features mean = ' + str(numpy.mean(features, axis=1)))

# rescale the features to [0,1]
feature_rescaling = RescaleFeatures()
feature_rescaling.init(features)
features.add_preprocessor(feature_rescaling)
features.apply_preprocessor()

print('mean after rescaling = ' + str(numpy.mean(features, axis=1)))

# remove mean from data
data = features.get_feature_matrix()
data = data.T
data-= numpy.mean(data, axis=0)
print numpy.mean(data, axis=0)

fig, axarr = pyplot.subplots(1,2)
axarr[0].matshow(numpy.cov(data.T))

#### whiten data

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
#!/usr/bin/env python2.7
#
# This software is distributed under BSD 3-clause license (see LICENSE file).
#
# Copyright (C) 2014 Thoralf Klein
#

from modshogun import RealFeatures, BinaryLabels, LibLinear
from numpy import random, mean

X_train = RealFeatures(random.randn(30, 100))
Y_train = BinaryLabels(random.randn(X_train.get_num_vectors()))

svm = LibLinear(1.0, X_train, Y_train)
svm.train()

Y_pred = svm.apply_binary(X_train)
Y_train.get_labels() == Y_pred.get_labels()

print "accuracy:", mean(Y_train.get_labels() == Y_pred.get_labels())

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import get_num_vectors [as 别名]
def statistics_hsic (n, difference, angle):
	from modshogun import RealFeatures
	from modshogun import DataGenerator
	from modshogun import GaussianKernel
	from modshogun import HSIC
	from modshogun import BOOTSTRAP, HSIC_GAMMA
	from modshogun import EuclideanDistance
	from modshogun import Math, Statistics, IntVector

	# init seed for reproducability
	Math.init_random(1)

	# note that the HSIC has to store kernel matrices
	# which upper bounds the sample size

	# use data generator class to produce example data
	data=DataGenerator.generate_sym_mix_gauss(n,difference,angle)
	#plot(data[0], data[1], 'x');show()

	# create shogun feature representation
	features_x=RealFeatures(array([data[0]]))
	features_y=RealFeatures(array([data[1]]))

	# compute median data distance in order to use for Gaussian kernel width
	# 0.5*median_distance normally (factor two in Gaussian kernel)
	# However, shoguns kernel width is different to usual parametrization
	# Therefore 0.5*2*median_distance^2
	# Use a subset of data for that, only 200 elements. Median is stable
	subset=IntVector.randperm_vec(features_x.get_num_vectors())
	subset=subset[0:200]
	features_x.add_subset(subset)
	dist=EuclideanDistance(features_x, features_x)
	distances=dist.get_distance_matrix()
	features_x.remove_subset()
	median_distance=Statistics.matrix_median(distances, True)
	sigma_x=median_distance**2
	features_y.add_subset(subset)
	dist=EuclideanDistance(features_y, features_y)
	distances=dist.get_distance_matrix()
	features_y.remove_subset()
	median_distance=Statistics.matrix_median(distances, True)
	sigma_y=median_distance**2
	#print "median distance for Gaussian kernel on x:", sigma_x
	#print "median distance for Gaussian kernel on y:", sigma_y
	kernel_x=GaussianKernel(10,sigma_x)
	kernel_y=GaussianKernel(10,sigma_y)

	hsic=HSIC(kernel_x,kernel_y,features_x,features_y)

	# perform test: compute p-value and test if null-hypothesis is rejected for
	# a test level of 0.05 using different methods to approximate
	# null-distribution
	statistic=hsic.compute_statistic()
	#print "HSIC:", statistic
	alpha=0.05

	#print "computing p-value using bootstrapping"
	hsic.set_null_approximation_method(BOOTSTRAP)
	# normally, at least 250 iterations should be done, but that takes long
	hsic.set_bootstrap_iterations(100)
	# bootstrapping allows usage of unbiased or biased statistic
	p_value_boot=hsic.compute_p_value(statistic)
	thresh_boot=hsic.compute_threshold(alpha)
	#print "p_value:", p_value_boot
	#print "threshold for 0.05 alpha:", thresh_boot
	#print "p_value <", alpha, ", i.e. test sais p and q are dependend:", p_value_boot<alpha

	#print "computing p-value using gamma method"
	hsic.set_null_approximation_method(HSIC_GAMMA)
	p_value_gamma=hsic.compute_p_value(statistic)
	thresh_gamma=hsic.compute_threshold(alpha)
	#print "p_value:", p_value_gamma
	#print "threshold for 0.05 alpha:", thresh_gamma
	#print "p_value <", alpha, ", i.e. test sais p and q are dependend::", p_value_gamma<alpha

	# sample from null distribution (these may be plotted or whatsoever)
	# mean should be close to zero, variance stronly depends on data/kernel
	# bootstrapping, biased statistic
	#print "sampling null distribution using bootstrapping"
	hsic.set_null_approximation_method(BOOTSTRAP)
	hsic.set_bootstrap_iterations(100)
	null_samples=hsic.bootstrap_null()
	#print "null mean:", mean(null_samples)
	#print "null variance:", var(null_samples)
	#hist(null_samples, 100); show()

	return p_value_boot, thresh_boot, p_value_gamma, thresh_gamma, statistic, null_samples