def example(num_samples=8, num_features=2, grid_size=20, filename="example2.pdf"):
l=[2.0,1.0,2.0,2.0,3.0,2.0,1.0,3.0,0.0,1.0,-1.0,-1.0,-1.0,-2.0,-2.0,-1.0] # here put your input training vectors
lmat=np.asarray(l)
lab=[1.0,1.0,1.0,1.0,-1.0,-1.0,-1.0,-1.0] # here put your labels
llab=np.asarray(lab)
samples = np.matrix(lmat # defining training matrix
.reshape(num_samples, num_features)) # re-shaping the matrix
labels = np.matrix(llab # defining training matrix
.reshape(num_samples, 1)) # re-shaping the matrix
trainer = svmpy.SVMTrainer(svmpy.Kernel.linear(), 0.1) # linear kernel
predictor = trainer.train(samples, labels)
plot(predictor, samples, labels, grid_size, filename)
# predict
print "training performed , predict !"
i=raw_input("space seperated coordinates of prediction vector : ")
try:
s=i.split()
x=[float(s[0]),float(s[1])]
except:
sys.exit(0)
try:
xmat=np.asarray(x)
xp=np.matrix(xmat # defining training matrix
.reshape(1, num_features)) # re-shaping the predict input
print predictor.predict(xp)
except:
sys.exit(0)
def example(num_samples=10, num_features=2, grid_size=20, filename="svm.pdf"):
samples = np.matrix(
np.random.normal(size=num_samples * num_features).reshape(
num_samples, num_features))
labels = 2 * (samples.sum(axis=1) > 0) - 1.0
trainer = svmpy.SVMTrainer(svmpy.Kernel.linear(), 0.1)
predictor = trainer.train(samples, labels)
plot(predictor, samples, labels, grid_size, filename)
def trainSVM(feature_samples, label_samples):
samples = feature_samples
labels = label_samples
trainer = svmpy.SVMTrainer(svmpy.Kernel.linear(), 0.01)
predictor = trainer.train(samples, labels)
return predictor
def example(num_samples=10, num_features=2, grid_size=20, filename="svm.pdf"):
samples = np.matrix(np.random.normal(size=num_samples * num_features) # defining training matrix
.reshape(num_samples, num_features)) # re-shaping the matrix
print type(samples)
labels = 2 * (samples.sum(axis=1) > 0) - 1.0
print labels
trainer = svmpy.SVMTrainer(svmpy.Kernel.linear(), 0.1) # linear kernel
predictor = trainer.train(samples, labels)
plot(predictor, samples, labels, grid_size, filename)
def trainSVM(feature_samples, label_samples):
num_samples=41
num_features=4
grid_size=20
samples = feature_samples
labels = label_samples
trainer = svmpy.SVMTrainer(svmpy.Kernel.linear(), 0.01)
predictor = trainer.train(samples, labels)
#plot(predictor, samples, labels, grid_size, filename)
return predictor
def example(num_samples=10,
num_features=784,
grid_size=20,
filename="svm.pdf"):
samples = np.matrix(
np.random.normal(size=num_samples * num_features).reshape(
num_samples, num_features))
samples = train_x
labels = 2 * (samples.sum(axis=1) > 0) - 1.0
labels = train_y - 2
print("sample variance :", np.var(samples))
trainer = svmpy.SVMTrainer(kernel.Kernel()._polykernel(120, 1), 0.1)
predictor = trainer.train(samples, labels)
predictAll(test_x, test_y, predictor)
def example(num_samples=10, num_features=2, grid_size=20, filename="svm.pdf"):
samples = np.matrix(
np.random.normal(size=num_samples * num_features).reshape(
num_samples, num_features))
print samples.shape
print samples
labels = 2 * (samples.sum(axis=1) > 0) - 1.0
print labels.shape
trainer = svmpy.SVMTrainer(svmpy.Kernel.linear(), 0.1)
predictor = trainer.train(samples, labels)
with open("predictor.pkl",
'wb') as output: # Overwrites any existing file.
pickle.dump(predictor, output, pickle.HIGHEST_PROTOCOL)
with open('predictor.pkl', 'rb') as input:
predictor = pickle.load(input)
plot(predictor, samples, labels, grid_size, filename)
def fit(self, curpus, sample_y):
word_indexes = []
word_counts = []
for row_curpus in curpus:
row_indexes = []
row_counts = []
for w_i, w_c in row_curpus:
row_indexes.append(w_i)
row_counts.append(w_c)
word_indexes.append(row_indexes)
word_counts.append(row_counts)
n_documents = len(word_indexes)
max_index = 0
for d in range(n_documents):
document_max = np.max(word_indexes[d])
if max_index < document_max:
max_index = document_max
n_word_types = max_index + 1
theta = np.random.uniform(size=(n_documents, self.n_topic))
old_theta = np.copy(theta)
phi = np.random.uniform(size=(self.n_topic, n_word_types))
svm_alpha = np.random.uniform(size=n_documents) + 1e-3
reg_weights = np.random.normal(size=self.n_topic)
for n in range(self.n_iter):
sum_phi = []
for k in range(self.n_topic):
sum_phi.append(sum(phi[k]))
ndk = theta
nkv = np.zeros((self.n_topic, n_word_types))
sample_X = []
for d in range(n_documents):
n_word_in_doc = len(word_indexes[d])
sum_theta_d = sum(theta[d])
prob_d = digamma(theta[d]) - digamma(sum_theta_d)
temp1 = svm_alpha[d] / n_word_in_doc * sample_y[d]
ndk[d, :] = 0.
dummies = np.array([0.] * self.n_topic)
for w in range(n_word_in_doc):
temp2 = temp1 * reg_weights[k]
word_no = word_indexes[d][w]
prob_w = digamma(phi[:, word_no]) - digamma(sum_phi)
latent_z = np.exp(prob_w + prob_d + temp2)
latent_z /= np.sum(latent_z)
ndk[d, :] += latent_z * word_counts[d][w]
nkv[:, word_no] += latent_z * word_counts[d][w]
z = np.argmax(latent_z)
dummies[z] += 1.
sample_X.append(dummies / len(word_indexes[d]))
theta = ndk + self.alpha
phi = nkv + self.beta
print(n, np.max(theta - old_theta))
old_theta = np.copy(theta)
t1 = time.time()
sample_X = np.array(sample_X)
trainer = svmpy.SVMTrainer(svmpy.Kernel.linear(), 0.1)
trainer.train(sample_X, sample_y.reshape(-1, 1))
svm_alpha = trainer.lagrange_multipliers
reg_weights = (svm_alpha * sample_y).T.dot(sample_X)
self.b = (sample_y - sample_X.dot(reg_weights)).mean()
print("svm train time:%.2fsec"%(time.time() - t1))
self.reg_weights = reg_weights
y_est = slda.predict(sample_X)
print("current accuracy", accuracy_score(y_est, sample_y))
for k in range(self.n_topic):
phi[k] = phi[k] / np.sum(phi[k])
for d in range(n_documents):
theta[d] = theta[d] / np.sum(theta[d])
self.reg_weights = reg_weights
return phi, theta, sample_X
"""
stoplist = set('for a of the and to in'.split())
texts = [[word for word in document.lower().split() if word not in stoplist]
for document in train_text]
dictionary = corpora.Dictionary(texts)
dictionary.save('/tmp/deerwester.dict')
corpus = [dictionary.doc2bow(text) for text in texts]
slda = SLDA(n_topic, 100)
phi, theta, sample_X = slda.fit(corpus, train_target)
y_est = slda.predict(sample_X)
print(y_est)
print("accuracy", accuracy_score(y_est, train_target))
#print(theta)
lda = vb.LDA(n_topic, 100)
phi, theta, sampe_X = lda.fit(corpus)
#svm = SVM(svm_alpha, n_iter=20000)
#reg_weights, self.b, svm_alpha = svm.fit(sample_X, sample_y)
trainer = svmpy.SVMTrainer(svmpy.Kernel.linear(), 0.1)
predictor = trainer.train(sample_X, train_target.reshape(-1, 1))
y_est = []
for x in sample_X:
y_est.append(predictor.predict(x))
print("accuracy", accuracy_score(y_est, train_target))
np.set_printoptions(precision=3, suppress=True)
