amkatrutsa

class Base(object):
    def __init__(self, a):
        self._a = a

class Derived(Base):
    def __init__(self, a={"a": 3}, b=10):
        super().__init__(a)
        self._b = b
    
    def update_a(self, a):

amkatrutsa

tf.reset_default_graph()
input_image = tf.placeholder(tf.float32, shape=[X_train_prep.shape[0], X_train_prep.shape[1]])
U = tf.Variable(tf.truncated_normal([X_train_prep.shape[0], hidden_dim], stddev=0.01), name="U")
V = tf.Variable(tf.truncated_normal([hidden_dim, initial_dim], stddev=0.01), name="V")
approximation_loss = tf.sqrt(tf.reduce_sum(tf.square(tf.sub(input_image, tf.matmul(U, V)))))

# Define optimizer and other constants

init = tf.initialize_all_variables()
sess = tf.InteractiveSession()

amkatrutsa

def get_P(n):
    val = [1, 2, 1] * (n / 2)
    col = []
    for i in xrange(n / 2):
        col += 3*[i]
    row = []
    for j in xrange((n-1)/2):
        row += range(2*j, 2*j + 3)
    P = scsp.csr_matrix((val, (row, col)), shape=(n, (n-1)/2))
    return 0.5 * P

amkatrutsa

k = 256  #number of clusters
c = 4   #number of subspaces
R = np.eye(m, m)
opq_codes = np.zeros((n, c))
opq_centroids = np.zeros((k, m/c, c))
iterations = 30
for it in xrange(iterations):
    print "Num iter", it
    mod_X = np.dot(X, R)
    opq_Y = np.zeros((n, m))

amkatrutsa

def compute_grad_metric(w, x1, x2, label1, label2):
    
    y = label1 == label2
    dx = (x1 - x2).reshape(64, 1)
    mod_x = w.dot(dx)
    if y:
        return 2 * np.dot(w, np.dot(dx, dx.T))
    elif (1 - np.dot(mod_x.T, mod_x) > 0):
        return -2 * np.dot(w, np.dot(dx, dx.T))
    else:

amkatrutsa

# First variant
term_1 = x
for k in xrange(2):
    print "Num iteration", k
    Wx = x
    for i in xrange(k+1):
        Wx = W.dot(Wx)
    WTWx = Wx
    for i in xrange(k+1):
        WTWx = W.T.dot(WTWx)

amkatrutsa

def process_line(line, t2i, i2t, outfile):
    ''' 
    Find all person's ID, They have to link to the category 
    'Living_people'
    '''
    pattern = "\((\d+),'(.*?)',(.*?)\)"
    current_page = None
    for match in finditer(pattern, line):
        topage, category, t = match.groups()
        if category == "Living_people":

amkatrutsa

# First version: len(person_id) = 15352
person_id = {}
    with open(in_dir + 'ID-title_dict.pickle', 'rb') as f:
        id2title = cPickle.load(f)
    with open(in_dir + 'person_id.txt') as f:
        for p_id in f:
            try:
                t = id2title[int(p_id)]
                person_id[int(p_id)] = 0
            except KeyError:

amkatrutsa

class MyVector { 
    int* vector;
    int size;
    public:
        MyVector() : vector(new int[1]), size(1) { }
        MyVector(int size) { 
            vector = new int[size]; 
            this->size = size; 
        }
        MyVector(int size, int num) {