vt = tuple(image_vectors)
vectors = np.vstack(vt)
# Do Principal Component Analysis on the matrix of the
# images
eig_vectors,eig_vals = pca.pca(vectors)
transformed_vectors = (np.dot(eig_vectors,vectors.T)).T
# This is just a hack to get the tags vector
# I just happen to know that the tag changes every
# 11 images because of how I loaded the images.
tags = [ ]
for i in range(transformed_vectors.shape[0]):
tags.append(i/11)
tags = np.asarray(tags)
tags.shape = (tags.shape[0],1)
K_NEIGHBORS = 3
#Leave one out test of the classifier
successes = 0.0
for i in range(transformed_vectors.shape[0]):
vec = np.vstack((transformed_vectors[:i,:],transformed_vectors[i+1:,:]))
tags_2 = np.vstack((tags[:i,:],tags[i+1:,:]))
classifier = classifiers.knnclassifier(vec,tags_2,K_NEIGHBORS)
classifier_output = classifier.classify(transformed_vectors[i,:])
if classifier_output == tags[i,0]:
successes += 1
#print 'Classifier output: ', classifier_output, 'Real Tag: ', tags[i,0]
print 'Success %', successes/transformed_vectors.shape[0]*100
for v in image_vectors:
v.shape = (1,160000)
vt = tuple(image_vectors)
vectors = np.vstack(vt)
# Do Principal Component Analysis on the matrix of the
# images
eig_vectors,eig_vals = pca.pca(vectors, threshold=0.99)
transformed_vectors = (np.dot(eig_vectors,vectors.T)).T
# This is just a hack to get the tags vector
# I just happen to know that the tag changes every
# 18 images because of how I loaded the images.
tags = [ ]
for i in range(transformed_vectors.shape[0]):
tags.append(i/18)
tags = np.asarray(tags)
tags.shape = (54,1)
#Leave one out test of the classifier
successes = 0.0
for i in range(transformed_vectors.shape[0]):
vec = np.vstack((transformed_vectors[:i,:],transformed_vectors[i+1:,:]))
tags_2 = np.vstack((tags[:i,:],tags[i+1:,:]))
classifier = classifiers.knnclassifier(vec,tags_2,5)
classifier_output = classifier.classify(transformed_vectors[i,:])
if classifier_output == tags[i,0]:
successes += 1
print 'Classifier output: ', classifier_output, 'Real Tag: ', tags[i,0]
print 'Success %', successes/transformed_vectors.shape[0]*100