def read_one_with_window(self, window_size, divide):
# sequence_matrix = array[size of 1st doc][data.x, data.y, data.z, data.gt]
sequence_matrix = self.get_sequence()
# d_x1 = array[size of 1st doc][x, y, z]
d_x = preprocess_sequence(sequence_matrix[:, 0 : self.n_in])
# d_x1 = preprocess_sequence(sequence_matrix[:, 0:self.n_in])
# d_y1 = array[size of 1st doc][labels]
d_y = sequence_matrix[:, self.n_in : self.n_in + 1].reshape(-1)
n_samples = d_x.shape[0] - window_size + 1
d_x_window = [d_x[i : i + window_size].flatten() for i in xrange(n_samples)]
d_y_window = [d_y[i + window_size / 2] for i in xrange(n_samples)]
if not divide:
set_x = theano.shared(numpy.asarray(d_x_window, dtype=theano.config.floatX), borrow=True)
set_y = T.cast(theano.shared(numpy.asarray(d_y_window, dtype=theano.config.floatX), borrow=True), "int32")
return (set_x, set_y)
data = zip(d_x_window, d_y_window)
visible_seqs = []
for label in xrange(7):
d_x_for_label = []
for row in data:
if row[-1] == label:
d_x_for_label.append(row[0])
# data_for_cur_label = all_data[numpy.where(all_data[:,1] == label)]
set_x = theano.shared(numpy.asarray(d_x_for_label, dtype=theano.config.floatX), borrow=True)
visible_seqs.append(set_x)
return visible_seqs
def read_all(self):
# sequence_matrix = array[size of 1st doc][data.x, data.y, data.z, data.gt]
sequence_matrix = self.get_sequence()
# d_x1 = array[size of 1st doc][x, y, z]
d_x1 = preprocess_sequence(sequence_matrix[:, 0 : self.n_in])
# d_y1 = array[size of 1st doc][labels]
d_y1 = sequence_matrix[:, self.n_in : self.n_in + 1].reshape(-1)
# data_x_ar = union for (x, y, z) coordinates in all files
data_x = d_x1
# data_y_ar = union for labels in all files
data_y = d_y1
for t in range(len(self.seqs) - 1):
# sequence_matrix = array[size of t-th doc][data.x, data.y, data.z, data.gt]
sequence_matrix = self.get_sequence()
# d_x = array[size of t-th doc][x, y, z]
d_x = preprocess_sequence(sequence_matrix[:, 0 : self.n_in])
# d_y = array[size of t-th doc][labels]
d_y = sequence_matrix[:, self.n_in : self.n_in + 1].reshape(-1)
# concatenate data in current file with data in prev files in one array
data_x = numpy.vstack((data_x, d_x))
data_y = numpy.concatenate((data_y, d_y))
gc.collect()
set_x = theano.shared(numpy.asarray(data_x, dtype=theano.config.floatX), borrow=True)
set_y = T.cast(theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX), borrow=True), "int32")
return (set_x, set_y)
def read_next_doc(self):
# sequence_matrix = array[size of doc][data.x, data.y, data.z, data.gt]
sequence_matrix = self.get_sequence()
# d_x = array[size of doc][x, y, z]
d_x = preprocess_sequence(sequence_matrix[:, 0 : self.n_in])
# d_y = array[size of doc][labels]
d_y = sequence_matrix[:, self.n_in : self.n_in + 1].reshape(-1)
data_x = d_x
data_y = d_y
gc.collect()
set_x = theano.shared(numpy.asarray(data_x, dtype=theano.config.floatX), borrow=True)
set_y = T.cast(theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX), borrow=True), "int32")
return (set_x, set_y)
