def tryLinearDiscriminantAnalysis(goFast):
from sklearn.datasets import dump_svmlight_file, load_svmlight_file
if goFast:
training_data, training_labels = load_svmlight_file("dt1_1500.trn.svm", n_features=253659, zero_based=True)
validation_data, validation_labels = load_svmlight_file("dt1_1500.vld.svm", n_features=253659, zero_based=True)
testing_data, testing_labels = load_svmlight_file("dt1_1500.tst.svm", n_features=253659, zero_based=True)
else:
training_data, training_labels = load_svmlight_file("dt1.trn.svm", n_features=253659, zero_based=True)
validation_data, validation_labels = load_svmlight_file("dt1.vld.svm", n_features=253659, zero_based=True)
testing_data, testing_labels = load_svmlight_file("dt1.tst.svm", n_features=253659, zero_based=True)
from sklearn.lda import LDA
from sklearn.metrics import accuracy_score
from sklearn.grid_search import ParameterGrid
from sklearn.decomposition import RandomizedPCA
rpcaDataGrid = [{"n_components": [10,45,70,100],
"iterated_power": [2, 3, 4],
"whiten": [True]}]
for rpca_parameter_set in ParameterGrid(rpcaDataGrid):
rpcaOperator = RandomizedPCA(**rpca_parameter_set)
rpcaOperator.fit(training_data,training_labels)
new_training_data = rpcaOperator.transform(training_data,training_labels)
new_validation_data = rpcaOperator.transform(validation_data,validation_labels)
ldaOperator = LDA()
ldaOperator.fit(new_training_data,training_labels)
print "Score = " + str(accuracy_score(validation_labels,ldaOperator.predict(new_validation_data)))
def test_randomized_pca_check_list():
"""Test that the projection by RandomizedPCA on list data is correct"""
X = [[1.0, 0.0], [0.0, 1.0]]
X_transformed = RandomizedPCA(n_components=1, random_state=0).fit(X).transform(X)
assert_equal(X_transformed.shape, (2, 1))
assert_almost_equal(X_transformed.mean(), 0.00, 2)
assert_almost_equal(X_transformed.std(), 0.71, 2)
def make_pca_datapoints(terms_map, stopwords, clusters):
new_terms_map = {}
raw_data = []
target = []
for line in open(tweets_file):
tokens = line.split()
terms = [terms_map[int(term)] for term in tokens[3].split(',') if terms_map[int(term)] not in stopwords]
for term in terms:
if not term in new_terms_map:
new_terms_map[term] = len(new_terms_map)
new_term_ids = [new_terms_map[term] for term in terms]
tags = [terms_map[int(term)] for term in tokens[4].split(',')]
raw_data.append(new_term_ids)
target.append(tags)
data = lil_matrix( (len(raw_data), len(new_terms_map)) )
count = 0
for cur_vector in raw_data:
for point in cur_vector:
data[(count, point)] += 1
count += 1
pca = RandomizedPCA (n_components=100)
transformed_data = pca.fit_transform(data)
xs = []
ys = []
count = 0
for datum in transformed_data:
for tag in target[count]:
if (len(tag) > 1) and tag[1:] in clusters:
xs.append(datum)
ys.append(clusters[tag[1:]])
count += 1
del transformed_data
return xs, ys
def build_classifier(train_data_x_in, train_data_y, classifier_in="svc_basic"):
print "Attempting to build classifier."
train_data_x = train_data_x_in
transformer = ""
# classifier = grid_search.GridSearchCV(svm.SVC(), parameters).fit(train_data_x, train_data_y)
if classifier_in == "svc_basic":
classifier = svm.SVC()
print "Selection was basic svm.SVC."
elif classifier_in == "svc_extensive":
classifier = svm.SVC(kernel="linear", C=0.025, gamma=0.01)
print "Selection was extensive svm.SVC, with linear kernel, C==0.025 and gamma==0.01."
elif classifier_in == "kneighbors_basic":
transformer = RandomizedPCA(n_components=2000)
train_data_x = transformer.fit_transform(train_data_x)
classifier = KNeighborsClassifier()
print "Selection was KNeighbors basic, using RandomizedPCA to transform data first. n_components==2000."
elif classifier_in == "bagging_basic":
classifier = BaggingClassifier(KNeighborsClassifier(), max_samples=0.5, max_features=0.5)
print "Selection was Bagging basic, with max_samples==0.5 and max_features==0.5."
elif classifier_in == "spectral_basic":
transformer = SpectralEmbedding(n_components=2000)
train_data_x = transformer.fit_transform(train_data_x)
classifier = KNeighborsClassifier()
print "Selection was Spectral basic, using svm.SVC with Spectral data fitting. n_components==2000."
# default to SVC in case of any sort of parsing error.
else:
print "Error in selecting classifier class. Reverting to SVC."
classifier = svm.SVC()
classifier.fit(train_data_x, train_data_y)
print "Doing classifier estimation."
return classifier, train_data_x, transformer
def _prepare_pca(self, data, max_n_components):
""" Helper Function """
from sklearn.decomposition import RandomizedPCA
# sklearn < 0.11 does not support random_state argument
kwargs = {'n_components': max_n_components, 'whiten': False}
aspec = inspect.getargspec(RandomizedPCA.__init__)
if 'random_state' not in aspec.args:
warnings.warn('RandomizedPCA does not support random_state '
'argument. Use scikit-learn to version 0.11 '
'or newer to get reproducible results.')
else:
kwargs['random_state'] = 0
pca = RandomizedPCA(**kwargs)
pca_data = pca.fit_transform(data.T)
if self._explained_var > 1.0:
if self.n_components is not None: # normal n case
self._comp_idx = np.arange(self.n_components)
to_ica = pca_data[:, self._comp_idx]
else: # None case
to_ica = pca_data
self.n_components = pca_data.shape[1]
self._comp_idx = np.arange(self.n_components)
else: # float case
expl_var = pca.explained_variance_ratio_
self._comp_idx = (np.where(expl_var.cumsum() <
self._explained_var)[0])
to_ica = pca_data[:, self._comp_idx]
self.n_components = len(self._comp_idx)
return to_ica, pca
def test_explained_variance():
"""Check that PCA output has unit-variance"""
rng = np.random.RandomState(0)
n_samples = 100
n_features = 80
X = rng.randn(n_samples, n_features)
pca = PCA(n_components=2).fit(X)
rpca = RandomizedPCA(n_components=2, random_state=42).fit(X)
assert_array_almost_equal(pca.explained_variance_,
rpca.explained_variance_, 1)
assert_array_almost_equal(pca.explained_variance_ratio_,
rpca.explained_variance_ratio_, 3)
# compare to empirical variances
X_pca = pca.transform(X)
assert_array_almost_equal(pca.explained_variance_,
np.var(X_pca, axis=0))
X_rpca = rpca.transform(X)
assert_array_almost_equal(rpca.explained_variance_,
np.var(X_rpca, axis=0))
# Compare with RandomizedPCA using sparse data
X = csr_matrix(X)
rpca = assert_warns(DeprecationWarning, rpca.fit, X)
assert_array_almost_equal(pca.explained_variance_,
rpca.explained_variance_, 1)
assert_array_almost_equal(pca.explained_variance_ratio_,
rpca.explained_variance_ratio_, 3)
def rpca(numpy_file='../data/Paintings/two_class/Paintings_train.csv'):
""" Performs randomized PCA on given numpy file.
Given a numpy file of n-rows and n-cols, where the last column is
the label and rest are features,n-rows are the samples.
:type numpy_file: string
:param numpy_file: The file name of numpy file to be analyzed.
"""
import numpy as np
import matplotlib.pyplot as pl
import pandas as pd
from sklearn.decomposition import RandomizedPCA
all_data = np.loadtxt(numpy_file,delimiter=',')
data = all_data[:,:-1]
y = all_data[:,-1]
pca = RandomizedPCA(n_components=2)
X = pca.fit_transform(data)
df = pd.DataFrame({"x": X[:, 0], "y": X[:, 1],\
"label":np.where(y==1, "realism", "abstract")})
colors = ["red", "yellow"]
for label, color in zip(df['label'].unique(), colors):
mask = df['label']==label
pl.scatter(df[mask]['x'], df[mask]['y'], c=color, label=label)
pl.legend()
pl.title('Randomized PCA analysis')
pl.show()
def SVM(X_train, y_train, X_test):
print("SVM with PCA of rbf, writening all on, no normalize")
preprocessing.normalize(X_train, 'max')
preprocessing.normalize(X_test, 'max')
#preprocessing.robust_scale(X, axis=1, with_centering = True) #bad
X_train = equalize_hist(X_train)
X_test = equalize_hist(X_test)
'''X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=TRAIN_TEST_SPLIT_RATIO)'''
n_components = 147
print("Extracting the top %d eigenfaces from %d faces"
% (n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=False).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
classifier13.fit(X_train_pca, y_train)
return list(classifier13.predict(X_test_pca))
def main():
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "Path to the image")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
rects, img = detect(image)
cropped = []
for idx, (x1, y1, x2, y2) in enumerate(rects):
crop_img = image[y1:y1 + (y2 - y1), x1:x1 + (x2 - x1)]
crop_img = cv2.resize(crop_img, (100,100), interpolation = cv2.INTER_AREA)
cv2.imshow("image" + str(idx), crop_img)
new_img = crop_img.reshape(crop_img.shape[0] * crop_img.shape[1], 3)
cropped.append(new_img.flatten())
# reduce feature size
cropped_pca = []
pca = RandomizedPCA(n_components=100)
cropped_pca = pca.fit_transform(cropped)
# training (hardcoded for now)
clf = SVC(probability=True)
train = cropped_pca[:7]
test = cropped_pca[7:13]
# clf.fit([[0,0],[1,1]], [1, 2])
clf.fit(train, [1,2,2,1,2,1,1])
for item in test:
print clf.predict_proba(item)
print clf.predict(item)
cv2.waitKey(0)
def SVM(X_data, y_data):
X_data = equalize_hist(X_data)
preprocessing.normalize(X_data, 'max')
preprocessing.scale(X_data, axis=1)
# preprocessing.normalize(X_data, 'max')
# X_data = equalize_hist(X_data)
# divide our data set into a training set and a test set
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X_data, y_data, test_size=TRAIN_TEST_SPLIT_RATIO)
n_components = 120
print("Extracting the top %d eigenfaces from %d faces"
% (n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
classifier = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
classifier.fit(X_train_pca, y_train)
print("====== PCA 150 ========")
print('TRAIN SCORE', classifier.score(X_train_pca, y_train))
print('TEST SCORE', classifier.score(X_test_pca, y_test))
def detect(self, imageURLs, params):
array = []
for param in params:
img = self.img_to_matrix(param['imageURL'])
data = self.flatten_image(img)
array.append(data)
array = np.array(array)
pca = RandomizedPCA(n_components=5)
n_data = pca.fit_transform(array)
clf = joblib.load('src/resource/models/model.pkl')
result = clf.predict(n_data).tolist()
for param, r in zip(params, result):
raw_img = urllib2.urlopen(param['imageURL']).read()
if r == 1:
cntr = len([i for i in os.listdir("test/images/rain/") if 'rain' in i]) + 1
path = "static/images/rain_" + str(cntr) + '.jpg'
f = open(path, 'wb')
f.write(raw_img)
f.close()
# イベント情報作成
when = {'type': 'timestamp', 'time':param['time']}
where = { "type": "Point", "coordinates": [param['longitude'], param['latitude']]}
what = {'topic': {'value':u'雨'}, 'tweet': param['value']}
who = [{"type": "url", "value": param['imageURL']},
{"value": "evwh <*****@*****.**>", "type": "author"}]
event = {'observation':{'what': what, 'when': when, 'where': where, 'who': who}}
self.connection['event']['TwitterImageRainSensor'].insert(event)
def test_sparse_randomized_pca_inverse():
"""Test that RandomizedPCA is inversible on sparse data"""
rng = np.random.RandomState(0)
n, p = 50, 3
X = rng.randn(n, p) # spherical data
X[:, 1] *= .00001 # make middle component relatively small
# no large means because the sparse version of randomized pca does not do
# centering to avoid breaking the sparsity
X = csr_matrix(X)
# same check that we can find the original data from the transformed signal
# (since the data is almost of rank n_components)
pca = RandomizedPCA(n_components=2, random_state=0)
assert_warns(DeprecationWarning, pca.fit, X)
Y = pca.transform(X)
Y_inverse = pca.inverse_transform(Y)
assert_almost_equal(X.todense(), Y_inverse, decimal=2)
# same as above with whitening (approximate reconstruction)
pca = assert_warns(DeprecationWarning, RandomizedPCA(n_components=2,
whiten=True, random_state=0).fit, X)
Y = pca.transform(X)
Y_inverse = pca.inverse_transform(Y)
relative_max_delta = (np.abs(X.todense() - Y_inverse)
/ np.abs(X).mean()).max()
# XXX: this does not seam to work as expected:
assert_almost_equal(relative_max_delta, 0.91, decimal=2)
def pca_data(test_x, train_x, params):
print 'pcaing data ...'
components = int(params['components'])
pca = RandomizedPCA(components, whiten=True).fit(train_x)
pca_train_x = pca.transform(train_x)
pca_test_x = pca.transform(test_x)
return pca_test_x, pca_train_x
def compute_pca(reception_stats,n_components=5):
reception_mean = reception_stats.mean(axis=0)
pca = RandomizedPCA(n_components-1)
pca.fit(reception_stats)
pca_components = np.vstack([reception_mean,pca.components_])
return pca,pca_components
def getPrincipleComponents(xtr, xte, n_components=50):
train = np.array(xtr)
test = np.array(xte)
pca = RandomizedPCA(n_components=n_components).fit(train)
xtrain = pca.transform(train)
xtest = pca.transform(test)
return xtrain, xtest
def do_pca(corr_matrix: _nested_ndarray, num_dim: int,
min_var_explanation: float =0.7) -> _nested_ndarray:
'''
This method performs PCA on a self-correlation matrix, reducing the number of columns to `num_dim`.
If such analysis does not sufficiently explain the underlying variance in the data, an exception is
thrown.
Args:
* `corr_matrix` - a square matrix of correlations
* `num_dim` - the number of dimensions to which the data should be reduced
* `min_var_explanation` - the minimum fraction of the underlying data variance that should be explained
Returns:
> A matrix of the PCA result on `corr_matrix`.
'''
num_dim = int(num_dim)
pca = PCA(n_components=num_dim, random_state=0)
pca_result = pca.fit_transform(corr_matrix)
var_ratio = pca.explained_variance_ratio_
if sum(var_ratio) < min_var_explanation:
raise PcaAccuracyException(
'PCA doesn\'t explain enough of the variance in the data')
return pca_result
def fit(self):
wordids_map = NameToIndex()
labs_map = NameToIndex()
wordscount = self._word_cluster.get_words_count()
print "start compute_tfidf ..."
#计算文档的词袋模型
docs = self._word_cluster.get_samples()
count =0
bow = []
labs = []
for k,v in docs.iteritems():
vec = numpy.zeros(wordscount).tolist()
for i in v:
vec[wordids_map.map(i)]+=1
bow.append(vec)
labs.append(labs_map.map(k[0]))
labs = numpy.array(labs)
tfidf = TfidfTransformer(smooth_idf=True, sublinear_tf=True,use_idf=True)
datas = numpy.array(tfidf.fit_transform(bow).toarray())
print "compute_tfidf done"
pca = RandomizedPCA(n_components=20, whiten=True).fit(datas)
svc = train_svc(numpy.array(labs_map.names), labs, pca.transform(datas))
self._tfidf = tfidf
self._svc = svc
self._labs_map = labs_map
self._wordids_map = wordids_map
self._pca = pca
def test_feature_union_weights():
# test feature union with transformer weights
iris = load_iris()
X = iris.data
y = iris.target
pca = RandomizedPCA(n_components=2, random_state=0)
select = SelectKBest(k=1)
# test using fit followed by transform
fs = FeatureUnion([("pca", pca), ("select", select)],
transformer_weights={"pca": 10})
fs.fit(X, y)
X_transformed = fs.transform(X)
# test using fit_transform
fs = FeatureUnion([("pca", pca), ("select", select)],
transformer_weights={"pca": 10})
X_fit_transformed = fs.fit_transform(X, y)
# test it works with transformers missing fit_transform
fs = FeatureUnion([("mock", TransfT()), ("pca", pca), ("select", select)],
transformer_weights={"mock": 10})
X_fit_transformed_wo_method = fs.fit_transform(X, y)
# check against expected result
# We use a different pca object to control the random_state stream
assert_array_almost_equal(X_transformed[:, :-1], 10 * pca.fit_transform(X))
assert_array_equal(X_transformed[:, -1],
select.fit_transform(X, y).ravel())
assert_array_almost_equal(X_fit_transformed[:, :-1],
10 * pca.fit_transform(X))
assert_array_equal(X_fit_transformed[:, -1],
select.fit_transform(X, y).ravel())
assert_equal(X_fit_transformed_wo_method.shape, (X.shape[0], 7))
def do_nbnn(train_folder, test_folder):
train = load_patches(args.train_folder)
test = load_patches(args.test_folder)
if options.relu:
get_logger().info("Applying RELU")
for class_data in train:
class_data.patches = class_data.patches.clip(min=0)
for class_data in test:
class_data.patches = class_data.patches.clip(min=0)
if options.scale:
get_logger().info("Applying standardization")
scaler = StandardScaler(copy=False)
scaler.fit(np.vstack([t.patches for t in train]))
for class_data in train:
class_data.patches = scaler.transform(class_data.patches)
for class_data in test:
class_data.patches = scaler.transform(class_data.patches)
if options.pca:
get_logger().info("Calculating PCA")
pca = RandomizedPCA(n_components=options.pca)
pca.fit(np.vstack([t.patches for t in train]))
#for class_data in train:
#get_logger().info("Fitting class " + class_data.name)
#pca.partial_fit(class_data.patches)
get_logger().info("Keeping " + str(pca.explained_variance_ratio_.sum()) + " variance (" + str(options.pca) +
") components\nApplying PCA")
for class_data in train:
class_data.patches = pca.transform(class_data.patches)
for class_data in test:
class_data.patches = pca.transform(class_data.patches)
nbnn(train, test, NN_Engine())
def main():
img_dir = 'images/'
images = [img_dir + f for f in os.listdir(img_dir)]
labels = [f.split('/')[-1].split('_')[0] for f in images]
label2ids = {v: i for i, v in enumerate(sorted(set(labels),
key=labels.index))}
y = np.array([label2ids[l] for l in labels])
data = []
for image_file in images:
img = img_to_matrix(image_file)
img = flatten_image(img)
data.append(img)
data = np.array(data)
# training samples
is_train = np.random.uniform(0, 1, len(data)) <= 0.7
train_X, train_y = data[is_train], y[is_train]
# training a classifier
pca = RandomizedPCA(n_components=5)
train_X = pca.fit_transform(train_X)
multi_svm = OneVsRestClassifier(LinearSVC())
multi_svm.fit(train_X, train_y)
# evaluating the model
test_X, test_y = data[is_train == False], y[is_train == False]
test_X = pca.transform(test_X)
print pd.crosstab(test_y, multi_svm.predict(test_X),
rownames=['Actual'], colnames=['Predicted'])
def test_explained_variance():
# Check that PCA output has unit-variance
rng = np.random.RandomState(0)
n_samples = 100
n_features = 80
X = rng.randn(n_samples, n_features)
pca = PCA(n_components=2).fit(X)
rpca = RandomizedPCA(n_components=2, random_state=rng).fit(X)
assert_array_almost_equal(pca.explained_variance_ratio_,
rpca.explained_variance_ratio_, 1)
# compare to empirical variances
X_pca = pca.transform(X)
assert_array_almost_equal(pca.explained_variance_,
np.var(X_pca, axis=0))
X_rpca = rpca.transform(X)
assert_array_almost_equal(rpca.explained_variance_, np.var(X_rpca, axis=0),
decimal=1)
# Same with correlated data
X = datasets.make_classification(n_samples, n_features,
n_informative=n_features-2,
random_state=rng)[0]
pca = PCA(n_components=2).fit(X)
rpca = RandomizedPCA(n_components=2, random_state=rng).fit(X)
assert_array_almost_equal(pca.explained_variance_ratio_,
rpca.explained_variance_ratio_, 5)
def reduce_features(features, var_explained=0.9, n_components=0, verbose=False):
"""
Performs feature reduction using PCA. Automatically selects nr. components
for explaining min_var_explained variance.
:param features: Features.
:param var_explained: Minimal variance explained.
:param n_components: Nr. of components.
:param exclude_columns: Columns to exclude.
:param verbose: Verbosity.
:return: Reduced feature set.
"""
if n_components == 0:
# Run full PCA to estimate nr. components for explaining given
# percentage of variance.
estimator = RandomizedPCA()
estimator.fit_transform(features)
variance = 0.0
for i in range(len(estimator.explained_variance_ratio_)):
variance += estimator.explained_variance_ratio_[i]
if variance > var_explained:
n_components = i + 1
if verbose:
print('{} % of variance explained using {} components'.format(var_explained, n_components))
break
# Re-run PCA with only estimated nr. components
estimator = RandomizedPCA(n_components=n_components)
features = estimator.fit_transform(features)
return features
def SVM(X, y):
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=TRAIN_TEST_SPLIT_RATIO)
print(len(X_train))
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 150
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
X_train_pca = equalize_hist(X_train_pca)
preprocessing.scale(X_train_pca * 1.0, axis=1)
X_test_pca = equalize_hist(X_test_pca)
preprocessing.scale(X_test_pca * 1.0, axis=1)
# classifier = svm.SVC(kernel='poly', degree = 3)
# classifier.fit(X_train, y_train)
# # print("======",3,"========")
# print('TRAIN SCORE', classifier.score(X_train, y_train))
# print('TEST SCORE', classifier.score(X_test, y_test))
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
classifier2 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
classifier2.fit(X_train_pca, y_train)
# print("======",3,"========")
print('TRAIN SCORE', classifier2.score(X_train_pca, y_train))
print('TEST SCORE', classifier2.score(X_test_pca, y_test))
def dimentionality_reduction(train_x , test_x): print "Dimentionality reduction to 10D on training and test data...." pca = RandomizedPCA(n_components=10) train_x = pca.fit_transform(train_x) test_x = pca.transform(test_x) print "Done." return train_x , test_x
def pca_estimator(data, targets, estimator, components_number=DEFAULT_COMPONENTS_NUMBER,
folds_number=DEFAULT_FOLDS_NUMBER):
kf = KFold(len(targets), n_folds=folds_number)
# 'scores' is numpy array. An index is a number of a fold. A value is a percent of right
# predicted samples from a test.
scores = np.zeros(folds_number)
start = time()
index = 0
for train, test in kf:
x_train, x_test, y_train, y_test = data[train], data[test], targets[train], targets[test]
pca = RandomizedPCA(n_components=components_number, whiten=True).fit(x_train)
x_train_pca = pca.transform(x_train)
x_test_pca = pca.transform(x_test)
clf = estimator.fit(x_train_pca, y_train)
scores[index] = clf.score(x_test_pca, y_test)
index += 1
# print("Iteration %d from %d has done! Score: %f" % (index, folds_number,
# scores[index - 1]))
finish = time()
return scores.mean(), scores.std() * 2, (finish - start)
def scatter(data, labels=None, title=None, name=None):
"""2d PCA scatter plot with optional class info
Return the pca model to be able to introspect the components or transform
new data with the same model.
"""
data = atleast2d_or_csr(data)
if data.shape[1] == 2:
# No need for a PCA:
data_2d = data
else:
pca = RandomizedPCA(n_components=2)
data_2d = pca.fit_transform(data)
for i, c, m in zip(np.unique(labels), cycle(COLORS), cycle(MARKERS)):
plt.scatter(data_2d[labels == i, 0], data_2d[labels == i, 1],
c=c, marker=m, label=i, alpha=0.5)
plt.legend(loc='best')
if title is None:
title = "2D PCA scatter plot"
if name is not None:
title += " for " + name
plt.xlabel('First Principal Component')
plt.ylabel('Second Principal Component')
plt.title(title)
return pca
def LogisticRegressionPCA(X, y):
# divide our data set into a training set and a test set
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X, y, test_size=TRAIN_TEST_SPLIT_RATIO)
# get randomized PCA model
num_components = 147
print("Extracting the top %d eigenfaces from %d faces"
% (num_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=num_components, whiten=True).fit(X_train)
# use the PCA model on our training set and test set.
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
h = .02 # step size in the mesh
logistic_regression = linear_model.LogisticRegression(C=1e5)
# we create an instance of Neighbours Classifier and fit the data.
logistic_regression.fit(X, y)
# print the performance of logistic regression
print("====== Logistic Regression with PCA ========")
print('TRAIN SCORE', logistic_regression.score(X_train, y_train))
print('TEST SCORE', logistic_regression.score(X_test, y_test))
def calc_hog(fpaths, save=False):
'''
Compute histogram of gradients (HOG). Saves in batches to prevent memory issues.
Input:
fpaths : files on which HOG will be computed
save : if true, output is saved to disk
'''
hogs = np.empty((len(fpaths), 15876))
for i, fpath in enumerate(fpaths):
img = imread(os.path.join(imgdir, fpath))
if len(img.shape)==3:
img = rgb2gray(img)
# rescale so all feature vectors are the same length
img_resize = resize(img, (128, 128))
img_hog = hog(img_resize)
hogs[i, :] = img_hog
hogs_sc = scale(hogs)
n_components = 15
pca = RandomizedPCA(n_components=n_components)
hogs_decomp = pca.fit_transform(hogs_sc)
df = pd.DataFrame(hogs_decomp, index=[os.path.split(i)[1] for i in fpaths])
df.index.name='fpath'
df.columns = ['feat_hog_%2.2u' % i for i in range(1, n_components+1)]
if save: df.to_csv('hog.csv')
return df
def pca_linear_initialization(self, data):
"""
We initialize the map, just by using the first two first eigen vals and eigenvectors
Further, we create a linear combination of them in the new map by giving values from -1 to 1 in each
X = UsigmaWT
XTX = Wsigma^2WT
T = XW = Usigma
// Transformed by W EigenVector, can be calculated by multiplication PC matrix by eigenval too
// Further, we can get lower ranks by using just few of the eigen vevtors
T(2) = U(2)sigma(2) = XW(2) ---> 2 is the number of selected eigenvectors
(*) Note that 'X' is the covariance matrix of original data
:param data: data to use for the initialization
:returns: initialized matrix with same dimension as input data
"""
cols = self.mapsize[1]
coord = None
pca_components = None
if np.min(self.mapsize) > 1:
coord = np.zeros((self.nnodes, 2))
pca_components = 2
for i in range(0, self.nnodes):
coord[i, 0] = int(i / cols) # x
coord[i, 1] = int(i % cols) # y
elif np.min(self.mapsize) == 1:
coord = np.zeros((self.nnodes, 1))
pca_components = 1
for i in range(0, self.nnodes):
coord[i, 0] = int(i % cols) # y
mx = np.max(coord, axis=0)
mn = np.min(coord, axis=0)
coord = (coord - mn)/(mx-mn)
coord = (coord - .5)*2
me = np.mean(data, 0)
data = (data - me)
tmp_matrix = np.tile(me, (self.nnodes, 1))
pca = RandomizedPCA(n_components=pca_components) # Randomized PCA is scalable
pca.fit(data)
eigvec = pca.components_
eigval = pca.explained_variance_
norms = np.sqrt(np.einsum('ij,ij->i', eigvec, eigvec))
eigvec = ((eigvec.T/norms)*eigval).T
for j in range(self.nnodes):
for i in range(eigvec.shape[0]):
tmp_matrix[j, :] = tmp_matrix[j, :] + coord[j, i]*eigvec[i, :]
self.matrix = np.around(tmp_matrix, decimals=6)
self.initialized = True
def rpca(train_X, test_X, n):
start_time = time.time()
pca = RandomizedPCA(n_components=n)
pca.fit(train_X.toarray())
train_X_pca = pca.transform(train_X.toarray())
test_X_pca = pca.transform(test_X.toarray())
print("--- %s seconds ---" % (time.time() - start_time))
return pca, train_X_pca, test_X_pca
def SVM(X, y):
print("SVM with PCA of rbf, writening all on, no normalize")
preprocessing.normalize(X, 'max')
#preprocessing.robust_scale(X, axis=1, with_centering = True) #bad
X = equalize_hist(X)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X, y, test_size=TRAIN_TEST_SPLIT_RATIO)
n_components = 120
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=False).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier13.fit(X_train_pca, y_train)
print("====== PCA 120 ========")
print('TRAIN SCORE', classifier13.score(X_train_pca, y_train))
print('TEST SCORE', classifier13.score(X_test_pca, y_test))
n_components = 130
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=False).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier13.fit(X_train_pca, y_train)
print("====== PCA 130 ========")
print('TRAIN SCORE', classifier13.score(X_train_pca, y_train))
print('TEST SCORE', classifier13.score(X_test_pca, y_test))
n_components = 147
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=False).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier13.fit(X_train_pca, y_train)
print("====== PCA 147 ========")
print('TRAIN SCORE', classifier13.score(X_train_pca, y_train))
print('TEST SCORE', classifier13.score(X_test_pca, y_test))
'''
def test_SVM(face_profile_data, face_profile_name_index, face_dim, face_profile_names):
"""
Testing: Build the SVM classification modle using the face_profile_data matrix (numOfFace X numOfPixel) and face_profile_name_index array, face_dim is a tuple of the dimension of each image(h,w) Returns the SVM classification modle
Parameters
----------
face_profile_data : ndarray (number_of_images_in_face_profiles, width * height of the image)
The pca that contains the top eigenvectors extracted using approximated Singular Value Decomposition of the data
face_profile_name_index : ndarray
The name corresponding to the face profile is encoded in its index
face_dim : tuple (int, int)
The dimension of the face data is reshaped to
face_profile_names: ndarray
The names corresponding to the face profiles
Returns
-------
clf : theano object
The trained SVM classification model
pca : theano ojbect
The pca that contains the top 150 eigenvectors extracted using approximated Singular Value Decomposition of the data
"""
X = face_profile_data
y = face_profile_name_index
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 150 # maximum number of components to keep
print("\nExtracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
eigenfaces = pca.components_.reshape((n_components, face_dim[0], face_dim[1]))
# This portion of the code is used if the data is scarce, it uses the number
# of imputs as the number of features
# pca = RandomizedPCA(n_components=None, whiten=True).fit(X_train)
# eigenfaces = pca.components_.reshape((pca.components_.shape[0], face_dim[0], face_dim[1]))
print("\nProjecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
# Train a SVM classification model
print("\nFitting the classifier to the training set")
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
# clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
# Train_pca Test Error Rate: 0.0670016750419
# Train_pca Test Recognition Rate: 0.932998324958
# clf = SVC(kernel='linear', C=1)
# 2452 samples from 38 people are loaded
# Extracting the top 150 eigenfaces from 1839 faces
# Extracting the top 150 eigenfaces from 1790 faces
# Train_pca Test Error Rate: 0.0904522613065
# Train_pca Test Recognition Rate: 0.909547738693
# clf = SVC(kernel='poly')
# Train_pca Test Error Rate: 0.201005025126
# Train_pca Test Recognition Rate: 0.798994974874
# clf = SVC(kernel='sigmoid')
# Train_pca Test Error Rate: 0.985318107667
# Train_pca Test Recognition Rate: 0.0146818923328
# clf = SVC(kernel='rbf').fit(X_train, y_train)
# Train_pca Test Error Rate: 0.0619765494137
# Train_pca Test Recognition Rate: 0.938023450586
# Best Estimator found using Radial Basis Function Kernal:
clf = SVC(C=1000.0, cache_size=200, class_weight='balanced', coef0=0.0,
decision_function_shape=None, degree=3, gamma=0.0001, kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
# Train_pca with Alex Test Error Rate: 0.088424437299
# Train_pca with Alex Test Recognition Rate: 0.911575562701
clf = clf.fit(X_train_pca, y_train)
# print("\nBest estimator found by grid search:")
# print(clf.best_estimator_)
###############################################################################
# Quantitative evaluation of the model quality on the test set
print("\nPredicting people's names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)
print("\nPrediction took %0.8f second per sample on average" % ((time() - t0)/y_pred.shape[0]*1.0))
# print "predicated names: ", y_pred
# print "actual names: ", y_test
error_rate = errorRate(y_pred, y_test)
print ("\nTest Error Rate: %0.4f %%" % (error_rate * 100))
print ("Test Recognition Rate: %0.4f %%" % ((1.0 - error_rate) * 100))
###############################################################################
# Testing
# X_test_pic1 = X_test[0]
# X_test_pic1_for_display = np.reshape(X_test_pic1, face_dim)
# t0 = time()
# pic1_pred_name = predict(clf, pca, X_test_pic1, face_profile_names)
# print("\nPrediction took %0.3fs" % (time() - t0))
# print "\nPredicated result for picture_1 name: ", pic1_pred_name
# for i in range(1,3): print ("\n")
# Display the picture
# plt.figure(1)
# plt.title(pic1_pred_name)
# plt.subplot(111)
# plt.imshow(X_test_pic1_for_display)
# plt.show()
###############################################################################
# Qualitative evaluation of the predictions using matplotlib
# import matplotlib.pyplot as plt
# def plot_gallery(images, titles, face_dim, n_row=3, n_col=4):
# """Helper function to plot a gallery of portraits"""
# plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))
# plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
# for i in range(n_row * n_col):
# plt.subplot(n_row, n_col, i + 1)
# plt.imshow(images[i].reshape(face_dim), cmap=plt.cm.gray)
# plt.title(titles[i], size=12)
# plt.xticks(())
# plt.yticks(())
# # plot the result of the prediction on a portion of the test set
# def title(y_pred, y_test, face_profile_names, i):
# pred_name = face_profile_names[y_pred[i]].rsplit(' ', 1)[-1]
# true_name = face_profile_names[y_test[i]].rsplit(' ', 1)[-1]
# return 'predicted: %s\ntrue: %s' % (pred_name, true_name)
# prediction_titles = [title(y_pred, y_test, face_profile_names, i)
# for i in range(y_pred.shape[0])]
# plot_gallery(X_test, prediction_titles, face_dim)
# # plot the gallery of the most significative eigenfaces
# eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
# plot_gallery(eigenfaces, eigenface_titles, face_dim)
# plt.show()
return clf, pca
def build_SVC(face_profile_data, face_profile_name_index, face_dim):
"""
Build the SVM classification modle using the face_profile_data matrix (numOfFace X numOfPixel) and face_profile_name_index array, face_dim is a tuple of the dimension of each image(h,w) Returns the SVM classification modle
Parameters
----------
face_profile_data : ndarray (number_of_images_in_face_profiles, width * height of the image)
The pca that contains the top eigenvectors extracted using approximated Singular Value Decomposition of the data
face_profile_name_index : ndarray
The name corresponding to the face profile is encoded in its index
face_dim : tuple (int, int)
The dimension of the face data is reshaped to
Returns
-------
clf : theano object
The trained SVM classification model
pca : theano ojbect
The pca that contains the top 150 eigenvectors extracted using approximated Singular Value Decomposition of the data
"""
X = face_profile_data
y = face_profile_name_index
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 10 # maximum number of components to keep
print("\nExtracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
eigenfaces = pca.components_.reshape((n_components, face_dim[0], face_dim[1]))
# This portion of the code is used if the data is scarce, it uses the number
# of imputs as the number of features
# pca = RandomizedPCA(n_components=None, whiten=True).fit(X_train)
# eigenfaces = pca.components_.reshape((pca.components_.shape[0], face_dim[0], face_dim[1]))
print("\nProjecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
# Train a SVM classification model
print("\nFitting the classifier to the training set")
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
# clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
# Best Estimator found using Radial Basis Function Kernal:
clf = SVC(C=1000.0, cache_size=200, class_weight='balanced', coef0=0.0,
decision_function_shape=None, degree=3, gamma=0.0001, kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
# Train_pca with Alex Test Error Rate: 0.088424437299
# Train_pca with Alex Test Recognition Rate: 0.911575562701
clf = clf.fit(X_train_pca, y_train)
# print("\nBest estimator found by grid search:")
# print(clf.best_estimator_)
###############################################################################
# Quantitative evaluation of the model quality on the test set
print("\nPredicting people's names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)
print("\nPrediction took %s per sample on average" % ((time() - t0)/y_pred.shape[0]*1.0))
# print "predicated names: ", y_pred
# print "actual names: ", y_test
error_rate = errorRate(y_pred, y_test)
print ("\nTest Error Rate: %0.4f %%" % (error_rate * 100))
print ("Test Recognition Rate: %0.4f %%" % ((1.0 - error_rate) * 100))
return clf, pca
mapping_targets = np.hstack(np.array(mapping_targets))
first_half = np.hstack(Data[0:no_mappings/2,:,:,:])
first_half = np.vstack(first_half)
second_half = np.hstack(Data[no_mappings/2:no_mappings,:,:,:])
second_half = np.vstack(second_half)
Data = np.vstack([first_half,second_half])
# for true targets uncomment next line
targets = np.hstack([targets,targets])
#for random targets uncomment next line
#targets = np.random.randint(1,no_locations+1,no_mappings*no_locations*no_thwacks)
lda = LDA(n_components=14)
pca = RandomizedPCA(n_components = 125)
classifier = KNeighborsClassifier(8)
proj = pca.fit_transform(Data)
proj = lda.fit_transform(proj,targets)
proj1 = pca.fit_transform(Data)
proj1 = lda.fit_transform(proj1,mapping_targets)
print(file)
plt.clf()
plt.scatter(proj[0:proj.shape[0]/2,0],proj[0:proj.shape[0]/2,1],c=targets[0:targets.shape[0]/2])
plt.title(file.rsplit('_')[0]+'_'+file.rsplit('_')[1]+" Before "+file.rsplit('_')[2]+" injection")
plt.colorbar()
plt.ylabel("LD1")
plt.xlabel("LD2")
plt.savefig(file.rsplit('_')[0]+'_'+file.rsplit('_')[1]+" Before "+file.rsplit('_')[2]+file[-11:-4]+" injection.svg")
plt.show()
plt.clf()
color='w',
zorder=10)
plt.title('Kmeans clustering on Pima dataset after ICA\n'
'Centroids are marked with white cross')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
file_name = 'Plots/Kmeans Pima after ICA ' + str(n_components) + '.png'
fig.savefig(file_name)
plt.close()
##############################################################################
# Visualize the results on RP-reduced data
reduced_data = RandomizedPCA(n_components=2).fit_transform(data)
kmeans = KMeans(init="random", n_clusters=n_components, n_init=10)
kmeans.fit(reduced_data)
# Step size of the mesh. Decrease to increase the quality of the VQ.
h = .02 # point in the mesh [x_min, m_max]x[y_min, y_max].
# Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = reduced_data[:, 0].min(), reduced_data[:, 0].max()
y_min, y_max = reduced_data[:, 1].min(), reduced_data[:, 1].max()
print(x_min, x_max, y_min, y_max)
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
fig = plt.figure()
plt.clf()
def pca_linear_initialization(self, data):
"""
We initialize the map, just by using the first two first eigen vals and eigenvectors
Further, we create a linear combination of them in the new map by giving values from -1 to 1 in each
X = UsigmaWT
XTX = Wsigma^2WT
T = XW = Usigma
// Transformed by W EigenVector, can be calculated by multiplication PC matrix by eigenval too
// Further, we can get lower ranks by using just few of the eigen vevtors
T(2) = U(2)sigma(2) = XW(2) ---> 2 is the number of selected eigenvectors
(*) Note that 'X' is the covariance matrix of original data
:param data: data to use for the initialization
:returns: initialized matrix with same dimension as input data
"""
cols = self.mapsize[1]
coord = None
pca_components = None
if np.min(self.mapsize) > 1:
coord = np.zeros((self.nnodes, 2))
pca_components = 2
for i in range(0, self.nnodes):
coord[i, 0] = int(i / cols) # x
coord[i, 1] = int(i % cols) # y
elif np.min(self.mapsize) == 1:
coord = np.zeros((self.nnodes, 1))
pca_components = 1
for i in range(0, self.nnodes):
coord[i, 0] = int(i % cols) # y
mx = np.max(coord, axis=0)
mn = np.min(coord, axis=0)
coord = (coord - mn) / (mx - mn)
coord = (coord - .5) * 2
me = np.mean(data, 0)
data = (data - me)
tmp_matrix = np.tile(me, (self.nnodes, 1))
pca = RandomizedPCA(
n_components=pca_components) # Randomized PCA is scalable
pca.fit(data)
eigvec = pca.components_
eigval = pca.explained_variance_
norms = np.sqrt(np.einsum('ij,ij->i', eigvec, eigvec))
eigvec = ((eigvec.T / norms) * eigval).T
for j in range(self.nnodes):
for i in range(eigvec.shape[0]):
tmp_matrix[
j, :] = tmp_matrix[j, :] + coord[j, i] * eigvec[i, :]
self.matrix = np.around(tmp_matrix, decimals=6)
self.initialized = True
def lininit(self):
#X = UsigmaWT
#XTX = Wsigma^2WT
#T = XW = Usigma #Transformed by W EigenVector, can be calculated by
#multiplication PC matrix by eigenval too
#Furthe, we can get lower ranks by using just few of the eigen vevtors
#T(2) = U(2)sigma(2) = XW(2) ---> 2 is the number of selected eigenvectors
# This is how we initialize the map, just by using the first two first eigen vals and eigenvectors
# Further, we create a linear combination of them in the new map by giving values from -1 to 1 in each
#Direction of SOM map
# it shoud be noted that here, X is the covariance matrix of original data
msize = getattr(self, 'mapsize')
rows = msize[0]
cols = msize[1]
nnodes = getattr(self, 'nnodes')
if np.min(msize)>1:
# set coordinates of the nodes in the grid (row, col)
coord = np.zeros((nnodes, 2))
for i in range(0, nnodes):
coord[i,0] = int(i / cols) #x
coord[i,1] = int(i % cols) #y
mx = np.max(coord, axis = 0)
mn = np.min(coord, axis = 0)
# normalize the coordinates between [-1,1]
coord = (coord - mn) / (mx - mn)
coord = (coord - .5) * 2
# for each column, shift data around its mean
data = getattr(self, 'data')
me = np.mean(data, 0)
data = (data - me)
# initialize codebook as a matrix dim * nnodes
codebook = np.tile(me, (nnodes, 1))
# pca
pca = RandomizedPCA(n_components=2) #Randomized PCA is scalable
#pca = PCA(n_components=2)
pca.fit(data)
eigvec = pca.components_
eigval = pca.explained_variance_
# compute the norms of the eigenvectors, normalize and multiply by eigenvalue
norms = np.sqrt(np.einsum('ij,ij->i', eigvec, eigvec))
eigvec = ((eigvec.T / norms) * eigval).T
# add the normalized eigenvector
for j in range(nnodes):
for i in range(eigvec.shape[0]):
codebook[j,:] += coord[j,i] * eigvec[i,:]
return np.around(codebook, decimals = 6)
elif np.min(msize) == 1:
coord = np.arange(nnodes)[:, np.newaxis]
mx = np.max(coord, axis = 0)
mn = np.min(coord, axis = 0)
# normalize the coordinates between [-1,1]
coord = (coord - mn) / (mx - mn)
coord = (coord - .5) * 2
# for each column, shift data around its mean
data = getattr(self, 'data')
me = np.mean(data, 0)
data = (data - me)
# initialize codebook as a matrix dim * nnodes
codebook = np.tile(me, (nnodes,1))
# pca
pca = RandomizedPCA(n_components=1) #Randomized PCA is scalable
pca.fit(data)
eigvec = pca.components_
eigval = pca.explained_variance_
# compute the norms of the eigenvectors, normalize and multiply by eigenvalue
norms = np.sqrt(np.einsum('ij,ij->i', eigvec, eigvec))
eigvec = ((eigvec.T/norms)*eigval).T; eigvec.shape
# add the normalized eigenvector
for j in range(nnodes):
for i in range(eigvec.shape[0]):
codebook[j,:] += coord[j,i]*eigvec[i,:]
return np.around(codebook, decimals = 6)
"""
from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import PCA as RandomizedPCA
import matplotlib.pyplot as plt
faces = fetch_lfw_people(min_faces_per_person=60)
print(faces.target_names)
print(faces.images.shape)
n_samples, h, w = faces.images.shape
print(n_samples)
n_components = 150
pca = RandomizedPCA(
n_components=n_components,
svd_solver='randomized') ##Randomized PCA for the the first 150 components
pca.fit(faces.data)
print(pca.components_) ##These are the first 150 Principal Components
pcacomponents25 = pca.components_[0:25] ##First 25 Principal Components
eigenfaces = pca.components_.reshape(
(n_components, h, w)) ##Eigenfaces for 150 PCs
eigenfaces25 = pcacomponents25.reshape(
(25, h, w)) ##Eigenfaces for first 25 PCs
## Plotting EigenFaces for First 25 PCs
fig, axes = plt.subplots(3,
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.datasets import fetch_lfw_people
faces = fetch_lfw_people(min_faces_per_person=60)
print(faces.target_names)
print(faces.images.shape)
from sklearn.decomposition import RandomizedPCA
pca = RandomizedPCA(150)
pca.fit(faces.data)
fig, axes = plt.subplots(3,
8,
figsize=(9, 4),
subplot_kw={
'xticks': [],
'yticks': []
},
gridspec_kw=dict(hspace=0.1, wspace=0.1))
for i, ax in enumerate(axes.flat):
ax.imshow(pca.components_[i].reshape(62, 47), cmap='bone')
plt.show()
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('number of components')
img = img_to_matrix(image)
img = flatten_image(img)
data.append(img)
data = np.array(data)
is_train = np.random.uniform(0, 1, len(data)) <= 0.7
y = np.where(np.array(labels)==query, 1, 0)
train_x, train_y = data[is_train], y[is_train]
test_x, test_y = data[is_train==False], y[is_train==False]
#add input to specify number of components to determine
UniqueImageComponents = int(raw_input("How many unique features are needed to distinguish between your image types? Choose a number between 2 and 6. "))
pca = RandomizedPCA(n_components=UniqueImageComponents)
X = pca.fit_transform(data)
make_plot(pd)
pl.show()
train_x = pca.fit_transform(train_x)
test_x = pca.transform(test_x)
print "Training and Test sets are created."
knn = KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
print "Running your machine learning model on the test set."
knn.fit(train_x, train_y)
result = knn.predict(test_x)
def find_and(n_components, plot):
print "Extracting the top %d eigenfaces from %d faces" % (n_components,
X_train.shape[0])
t0 = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print "done in %0.3fs" % (time() - t0)
eigenfaces = pca.components_.reshape((n_components, h, w))
# print 'components', pca.explained_variance_ratio_, pca.components_,
print 'components1', pca.explained_variance_ratio_[0]
print 'components1', pca.explained_variance_ratio_[1]
print "Projecting the input data on the eigenfaces orthonormal basis"
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print "done in %0.3fs" % (time() - t0)
###############################################################################
# Train a SVM classification model
print "Fitting the classifier to the training set"
t0 = time()
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
# for sklearn version 0.16 or prior, the class_weight parameter value is 'auto'
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
clf = clf.fit(X_train_pca, y_train)
print "done in %0.3fs" % (time() - t0)
print "Best estimator found by grid search:"
print clf.best_estimator_
###############################################################################
# Quantitative evaluation of the model quality on the test set
print "Predicting the people names on the testing set"
t0 = time()
y_pred = clf.predict(X_test_pca)
print "done in %0.3fs" % (time() - t0)
print classification_report(y_test, y_pred, target_names=target_names)
print confusion_matrix(y_test, y_pred, labels=range(n_classes))
prediction_titles = [
title(y_pred, y_test, target_names, i) for i in range(y_pred.shape[0])
]
if plot == True:
plot_gallery(X_test, prediction_titles, h, w)
# plot the gallery of the most significative eigenfaces
eigenface_titles = [
"eigenface %d" % i for i in range(eigenfaces.shape[0])
]
plot_gallery(eigenfaces, eigenface_titles, h, w)
pl.show()
random_pca_data_50 = normalization('gene_IndividualsArr.pkl',
'top10Genes_Indiv.pkl')
random_pca_data_25 = normalization('gene_IndividualsArr.pkl',
'top10Genes_Indiv.pkl')
random_pca_data_10 = normalization('gene_IndividualsArr.pkl',
'top10Genes_Indiv.pkl')
pca_data = normalization('gene_IndividualsArr.pkl', 'top10Genes_Indiv.pkl')
sparse_pca_data = normalization('gene_IndividualsArr.pkl',
'top10Genes_Indiv.pkl')
kernel_pca_data = normalization('gene_IndividualsArr.pkl',
'top10Genes_Indiv.pkl')
fast_ica_data = normalization('gene_IndividualsArr.pkl',
'top10Genes_Indiv.pkl')
nmf_data = normalization('gene_IndividualsArr.pkl', 'top10Genes_Indiv.pkl')
random_pca_50 = RandomizedPCA(n_components=50)
random_pca_model_50 = random_pca_50.fit(random_pca_data_50)
random_X_new_50 = random_pca_50.fit_transform(X)
print 'random_pca_50 explained', random_pca_50.explained_variance_ratio_
print 'random_pca_50 explained sum', sum(
random_pca_50.explained_variance_ratio_)
joblib.dump(random_pca_model_50, 'random_pca_model_50.pkl')
joblib.dump(random_pca_50.explained_variance_ratio_,
'random_pca_50.explained_variance_ratio_.pkl')
joblib.dump(random_X_new_50, 'random_X_new_50.pkl')
random_pca_25 = RandomizedPCA(n_components=25)
random_pca_model_25 = random_pca_25.fit(random_pca_data_25)
random_X_new_25 = random_pca_25.fit_transform(X)
print 'random_pca_25 explained', random_pca_25.explained_variance_ratio_
print 'random_pca_25 explained sum', sum(
print "n_features: %d" % n_features print "n_classes: %d" % n_classes ############################################################################### # Split into a training and testing set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42) ############################################################################### # Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled # dataset): unsupervised feature extraction / dimensionality reduction n_components = 250 print "Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0]) t0 = time() pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train) print "done in %0.3fs" % (time() - t0) eigenfaces = pca.components_.reshape((n_components, h, w)) print "Projecting the input data on the eigenfaces orthonormal basis" t0 = time() X_train_pca = pca.transform(X_train) X_test_pca = pca.transform(X_test) print "done in %0.3fs" % (time() - t0) ############################################################################### # Train a SVM classification model print "Fitting the classifier to the training set"
print "n_features: %d" % n_features print "n_classes: %d" % n_classes ############################################################################### # Split into a training and testing set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42) ############################################################################### # Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled # dataset): unsupervised feature extraction / dimensionality reduction n_components = 150 print "Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0]) t0 = time() pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train) print "done in %0.3fs" % (time() - t0) eigenfaces = pca.components_.reshape((n_components, h, w)) print "pca component variance ", pca.explained_variance_[:2] print "Projecting the input data on the eigenfaces orthonormal basis" t0 = time() X_train_pca = pca.transform(X_train) X_test_pca = pca.transform(X_test) print "done in %0.3fs" % (time() - t0) ############################################################################### # Train a SVM classification model
# Encode the dependent variable
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)
Y_Results = np.array(['0=Block', '1=NB-No_Block', '2=NB-Wait', '3=No_Block'])
print(Y_Results)
print(Y[:10])
# Part B: Run Random Component Analysis (RCA) algorithm
# Scale the independent variobles
sc = StandardScaler()
X = sc.fit_transform(X)
# Apply RCA to the independent variables
rca = RCA(random_state=1)
X_new = rca.fit_transform(X)
var = rca.explained_variance_ratio_
print(pd.DataFrame(var[:10]))
# Part C: Use dimensionally reduced dataset to cluster
# Using the elbow method to find the optimal number of clusters
wcss = []
for i in range(1, 15):
kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 1)
kmeans.fit(X_new)
wcss.append(kmeans.inertia_)
plt.plot(range(1, 15), wcss)
plt.title('Finding the Best K: The Elbow Method')
plt.xlabel('Number of clusters')
Use PCA to reconstruct some of the MNIST test digits. """ # My libraries import mnist_loader # Third-party libraries import matplotlib import matplotlib.pyplot as plt import numpy as np from sklearn.decomposition import RandomizedPCA # Training training_data, test_inputs, actual_test_results = mnist_loader.load_data_nn() pca = RandomizedPCA(n_components=30) nn_images = [x for (x, y) in training_data] pca_images = np.concatenate(nn_images, axis=1).transpose() pca_r = pca.fit(pca_images) # Try PCA on first ten test images test_images = np.array(test_inputs[:10]).reshape((10, 784)) test_outputs = pca_r.inverse_transform(pca_r.transform(test_images)) # Plot the first ten test images and the corresponding outputs fig = plt.figure() ax = fig.add_subplot(111) images_in = [test_inputs[j].reshape(-1, 28) for j in range(10)] images_out = [test_outputs[j].reshape(-1, 28) for j in range(10)] image_in = np.concatenate(images_in, axis=1) image_out = np.concatenate(images_out, axis=1)
plot_digits(filtered)
show()
#Example Eigenfaces
#get out face data
from sklearn.datasets import fetch_lfw_people
faces = fetch_lfw_people(min_faces_per_person=60)
print(faces.target_names)
print(faces.images.shape)
#we will use RandomizedPCA since this is a large dataset
#we will reduce from near 3000 to 150 components
from sklearn.decomposition import PCA as RandomizedPCA
pca = RandomizedPCA(150)
pca.fit(faces.data)
fig, axes = plt.subplots(3, 8, figsize=(9, 4),
subplot_kw={'xticks':[], 'yticks':[]},
gridspec_kw=dict(hspace=0.1, wspace=0.1))
for i, ax in enumerate(axes.flat):
ax.imshow(pca.components_[i].reshape(62, 47), cmap='bone')
show()
#lets check the cumulative variance
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('number of components')
plt.ylabel('cumulative explained variance');
#150 turns out to be around 90% of variance
#lets compare to the full data
## Do the tensor factorization
np.random.seed(seed)
M, cpstats, mstats = CP_APR.cp_apr(trainX, R, maxiters=outerIter, maxinner=10)
M.normalize_sort(1)
# zero out the small factors
for n in range(1,2):
zeroIdx = np.where(M.U[n] < zeroThr)
M.U[n][zeroIdx] = 0
klp = KLProjection.KLProjection(M.U, M.R)
ptfFeat = klp.projectSlice(X, 0)
ptfMatrix = khatrirao.khatrirao(M.U[1], M.U[2])
dbOutput = getDBEntry("CP-APR", ptfMatrix)
## now we want to do PCA and NMF as well
flatX = sptenmat.sptenmat(X, [0]).tocsrmat() # matricize along the first mode
pcaModel = RandomizedPCA(n_components=R)
pcaModel.fit(flatX[train, :])
pcaFeat = pcaModel.transform(flatX)
pcaBasis = pcaModel.components_
dbOutput = np.vstack((dbOutput, getDBEntry("PCA", pcaBasis)))
nmfModel = nimfa.mf(flatX[train,:], method="nmf", max_iter=outerIter, rank=R)
nmfResult = nimfa.mf_run(nmfModel)
nmfFeat = nmfTransform(R, nmfResult, flatX)
## get the basis to be stored off
nmfBasis = nmfResult.coef().transpose()
nmfBasis = preprocessing.normalize(nmfBasis, norm="l1", axis=0)
nmfBasis = nmfBasis.toarray()
zeroIdx = np.where(nmfBasis < zeroThr*zeroThr)
nmfBasis[zeroIdx]= 0
dbOutput = np.vstack((dbOutput, getDBEntry("NMF", nmfBasis)))
print(faces.target_names)
print(faces.images.shape)
fig, ax = plt.subplots(3, 5)
for i, axi in enumerate(ax.flat):
axi.imshow(faces.images[i], cmap='bone')
axi.set(xticks=[], yticks=[],
xlabel=faces.target_names[faces.target[i]])
from sklearn.svm import SVC
from sklearn.decomposition import RandomizedPCA
from sklearn.pipeline import make_pipeline
pca = RandomizedPCA(n_components=150, whiten=True, random_state=42)
svc = SVC(kernel='rbf', class_weight='balanced')
model = make_pipeline(pca, svc)
from sklearn.cross_validation import train_test_split
Xtrain, Xtest, ytrain, ytest = train_test_split(faces.data, faces.target,
random_state=42)
from sklearn.grid_search import GridSearchCV
param_grid = {'svc__C': [1, 5, 10, 50],
'svc__gamma': [0.0001, 0.0005, 0.001, 0.005]}
grid = GridSearchCV(model, param_grid)
get_ipython().run_line_magic("time", " grid.fit(Xtrain, ytrain)")
def fit_deprecated(X):
global Y
rpca = RandomizedPCA(random_state=0)
Y = rpca.fit_transform(X)
cells_per_block=self.cells_per_block,
)
result.append(features)
return np.array(result)
MODELS = {
'linearsvc': (
LinearSVC(),
{
'C': [0.01, 0.1, 1.0]
},
),
'linearsvc-pca': (
Pipeline([
('pca', RandomizedPCA(n_components=100, whiten=True)),
('clf', LinearSVC(C=1.0)),
]),
{
'pca__n_components': [10, 30, 100],
'clf__C': [0.01, 0.1, 1.0]
},
),
'linearsvc-hog': (
Pipeline([
('hog',
HOGFeatures(
orientations=8,
pixels_per_cell=(4, 4),
cells_per_block=(3, 3),
)),
plot_digits(noisy)
pca = PCA(.5).fit(noisy)
pca.n_components_
components = pca.transform(noisy)
filtered = pca.inverse_transform(components)
plot_digits(filtered)
from sklearn.datasets import fetch_lfw_people
faces = fetch_lfw_people(min_faces_per_person=60)
print(faces.target_names)
print(faces.images.shape)
from sklearn.decomposition import RandomizedPCA
pca = RandomizedPCA(150)
pca.fit(faces.data)
# print eigenfaces
fig, axes = plt.subplots(3, 8, figsize=(9, 4),
subplot_kw = {'xticks' : [], 'yticks' : []},
gridspec_kw = dict(hspace=.1, wspace=.1))
for i, ax in enumerate(axes.flat):
ax.imshow(pca.components_[i].reshape(62,47), cmap='bone')
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('number of components')
plt.ylabel('cumulative explained variance')
pca = RandomizedPCA(150).fit(faces.data)
test_files_count = data['test_files_count']
validationOriginalImage = data['validationOriginalImage']
#print valid_files
print train_data.shape
print valid_data.shape
# record time used for training
start = time.clock()
for i in range(0, len(superpixels)):
print np.max(superpixels[i][0])
# Preprocessing normalize data
scaler = StandardScaler()
scaler.fit(train_data)
train_data = scaler.transform(train_data)
# Preprocessing RandomizePCA
pca = RandomizedPCA(n_components=15)
pca.fit(train_data)
#train_data = pca.transform(train_data)
print train_data.shape
# set classifier and fit data
clf = chooseClassification('RF')
clf = clf.fit(train_data, train_labels.ravel())
#scores = cross_val_score(clf, train_data, train_label)
#scores.mean()
# benchmark using validation data
valid_data = scaler.transform(valid_data)
#valid_data = pca.transform(valid_data)
#print clf.predict_proba(valid_data[0])
#wait = input("PRESS ENTER TO CONTINUE.")
import input_data_svm
datasets = input_data_svm.read_data_sets()
X = np.vstack((datasets.train_set.inputs(), datasets.validation_set.inputs()))
y = np.hstack((datasets.train_set.targets(), datasets.validation_set.targets()))
# X = datasets.train_set.inputs()
# y = datasets.train_set.targets()
X = X[:]
y = y[:]
# Reduce the dimensionality of the dataset
print("Applying PCA to reduce dimensions")
pca = RandomizedPCA(n_components=PCA_COMPONENTS, whiten=True).fit(X)
# eigenfaces = pca.componenets_reshape((PCA_COMPONENTS, h, w))
X = pca.transform(X)
print("Finished PCA preprocessing")
# Normalize the data
scaler = StandardScaler()
X = scaler.fit_transform(X)
##############################################################################
# Train classifiers
#
# For an initial search, a logarithmic grid with basis
# 10 is often helpful. Using a basis of 2, a finer
# tuning can be achieved but at a much higher cost.
import numpy as np from sklearn.datasets import make_circles from sklearn.ensemble import RandomTreesEmbedding, ExtraTreesClassifier from sklearn.decomposition import RandomizedPCA from sklearn.naive_bayes import BernoulliNB # make a synthetic dataset X, y = make_circles(factor=0.5, random_state=0, noise=0.05) # use RandomTreesEmbedding to transform data hasher = RandomTreesEmbedding(n_estimators=10, random_state=0, max_depth=3) X_transformed = hasher.fit_transform(X) # Visualize result using PCA pca = RandomizedPCA(n_components=2) X_reduced = pca.fit_transform(X_transformed) # Learn a Naive Bayes classifier on the transformed data nb = BernoulliNB() nb.fit(X_transformed, y) # Learn an ExtraTreesClassifier for comparison trees = ExtraTreesClassifier(max_depth=3, n_estimators=10, random_state=0) trees.fit(X, y) # scatter plot of original and reduced data fig = pl.figure(figsize=(9, 8)) ax = pl.subplot(221) ax.scatter(X[:, 0], X[:, 1], c=y, s=50)
discretizer3=discretizer(3),
discretizer10=discretizer(10),
kmeans3=clusterizer(MiniBatchKMeans(3)),
kmeans10=clusterizer(MiniBatchKMeans(10)),
kmeans_gap=clusterizer(FitClusterer(min_clusters=3)),
ward3=clusterizer(Ward(3)),
ward10=clusterizer(Ward(10)),
meanshift=clusterizer(MeanShift()),
# spectral3=clusterizer(SpectralClustering(3)), # FIXME
# spectral10=clusterizer(SpectralClustering(10)), # FIXME
affinity_prop=clusterizer(AffinityPropagation()),
dbscan=clusterizer(DBSCAN()),
)
BINARY_TO_NUMERICAL = dict(identity=identity, )
BINARY_TO_CATEGORICAL = dict(identity=identity, )
CATEGORICAL_TO_NUMERICAL = dict(
noop=identity,
binarize=binary_transformer(),
pca1=binary_transformer(RandomizedPCA(1)),
# ica1=binary_transformer(FastICA(1)), # FIXME
median_ordinal_pred=discrete_ordinal_predictor("median"),
mean_ordinal_pred=discrete_ordinal_predictor("mean"),
max_ordinal_pred=discrete_ordinal_predictor("max"),
min_ordinal_pred=discrete_ordinal_predictor("min"),
)
CATEGORICAL_TO_CATEGORICAL = dict(identity=identity, )
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.decomposition import RandomizedPCA
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import FunctionTransformer
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE',
delimiter='COLUMN_SEPARATOR',
dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1),
tpot_data.dtype.names.index('class'),
axis=1)
training_features, testing_features, training_classes, testing_classes = \
train_test_split(features, tpot_data['class'], random_state=42)
exported_pipeline = make_pipeline(
make_union(
VotingClassifier([("est",
KNeighborsClassifier(n_neighbors=5,
weights="uniform"))]),
FunctionTransformer(lambda X: X)), RandomizedPCA(iterated_power=1),
RandomForestClassifier(n_estimators=500))
exported_pipeline.fit(training_features, training_classes)
results = exported_pipeline.predict(testing_features)
from sklearn.cross_validation import train_test_split
features_train, features_test, labels_train, labels_test = \
train_test_split(features_scaled, labels, test_size=0.25, random_state=42)
##features_train_pca = pca.transform(features_train)
##features_test_pca = pca.transform(features_test)
#from sklearn.pipeline import Pipeline
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.neighbors import KNeighborsClassifier
#from sklearn.grid_search import GridSearchCV
estimator_svm = [('reduce_dim', RandomizedPCA()),
('clf_svm',
SVC(kernel='rbf', class_weight='balanced', gamma=0.1,
C=1000))]
estimator_tree = [('reduce_dim', RandomizedPCA()),
('clf_tree',
DecisionTreeClassifier(criterion='entropy',
max_features='sqrt',
splitter='best'))]
#estimator_knn = [('reduce_dim', PCA()), ('clf_knn', KNeighborsClassifier())]
#estimator_rf = [('reduce_dim', PCA()), ('clf_rf', RandomForestClassifier())]
#estimator_ab = [('reduce_dim', PCA()), ('clf_ab', AdaBoostClassifier())]
#param_svm = {
# 'kernel': ['linear', 'poly', 'rbf'],
# 'C': [10, 100, 1000, 10000],
# Create an array with flattened images X
# and an array with ID of the people on each image y
X = np.zeros([NUM_TRAINIMAGES, IMG_RES], dtype='int8')
names = []
# Populate training array with flattened imags from subfolders of train_faces and names
c = 0
for x, folder in enumerate(folders):
train_faces = glob.glob(folder + '/*')
for i, face in enumerate(train_faces):
X[c, :] = prepare_image(face)
names.append(ID_from_filename(face))
c = c + 1
# perform principal component analysis on the images
pca = RandomizedPCA(n_components=NUM_EIGENFACES, whiten=True).fit(X)
X_pca = pca.transform(X)
#'''
while (True):
_, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Detect faces in the image
faces = faceCascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
for (x, y, w, h) in faces:
if len(faces) == 1:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
gray = gray[y:y + h, x:x + w]
s = cv2.resize(gray, (92, 112))
Snap(s)
def doPCA(data, dimensions=2):
from sklearn.decomposition import RandomizedPCA
model = RandomizedPCA(n_components=dimensions)
model.fit(data)
return model