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()
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 "Train ok!"
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!"
