def reshape_dataset(dataset):
print dataset.shape
#You need fill as your program
x, y = dataset.shape
feature_data = dataset[:, :-1]
temp_data = np.zeros((x, y - 1, y - 1))
input_data = np.zeros((x, y + 2, y + 2))
for i in xrange(x):
for j in xrange(y - 1):
right = feature_data[i, :j]
left = feature_data[i, j:]
temp_data[i, j] = np.concatenate((left, right))
input_data[:, 1:-2, 1:-2] = temp_data[:, :, :]
input_data = input_data.reshape((x, y + 2, y + 2, 1))
output_data = dataset[:, -1].astype(int)
output_data = dpm.num_to_one_hot(output_data, 9)
print input_data.shape
return input_data, output_data
def reshape_dataset(dataset, SPAN):
input_data = np.zeros((dataset.shape[0], 32, 104, 2))
temp_data = np.reshape(dataset[:, :6200], (-1, 31, 100, 2))
input_data[:, :31, 2:102,
0] = temp_data[:, :, :, 0] # cause input size is 32 not 31
input_data[:, :31, 2:102, 1] = temp_data[:, :, :, 1]
para_data = dataset[:, 6200:6241]
output_data = dataset[:, 6240 + SPAN[0]].astype(int)
output_data = dpm.num_to_one_hot(output_data, 3)
return input_data, para_data, output_data
def reshape_dataset(dataset, SPAN):
#You need fill as your program
input_data = np.zeros((dataset.shape[0], 304, 48, 2))
real_C = np.reshape(dataset[:, :12000], (dataset.shape[0], 300, 40))
imag_C = np.reshape(dataset[:, 12000:24000], (dataset.shape[0], 300, 40))
input_data[:, 2:302, 4:44, 0] = real_C[:, :, :]
input_data[:, 2:302, 4:44, 1] = imag_C[:, :, :]
para_data = dataset[:, 24000:24041]
#cause span begin with 1 not 0
output_data = dataset[:, 24061 + SPAN[0] - 1].astype(int)
output_data = dpm.num_to_one_hot(output_data, 6)
return input_data, para_data, output_data
def reshape_dataset(dataset):
#You need fill as your program]
#x is batch size y is 94
feature_data = dataset[:,:-1]
temp_data = triple_size_data(feature_data)
x, y, _, _ = temp_data.shape
input_data = np.zeros((x, y+3, y+3, y))
input_data[:,1:-2,1:-2,:] = temp_data[:,:,:]
output_data = dataset[:, -1].astype(int)
output_data = dpm.num_to_one_hot(output_data, 9)
return input_data, output_data
def reshape_dataset(dataset):
#You need fill as your program]
#feature data (x,35)
feature_data = dataset[:, :-1]
#temp data (x,35,35,35)
temp_data = triple_size_data(feature_data)
#x,y (batch,35)
x, y, _, _ = temp_data.shape
#input data (batch, 40,40,35)
input_data = np.zeros((x, y + 5, y + 5, y))
input_data[:, 2:-3, 2:-3, :] = temp_data[:, :, :]
output_data = dataset[:, -1].astype(int)
output_data = dpm.num_to_one_hot(output_data, 2)
return input_data, output_data