def processData(data):
data = pu.dropData(np.array([0, 0, 0, 0, 0, 0, 0, 0]), data)
data = np.delete(data, [0, 1, 2], 0)
data = pu.dropData(np.array([0, 0, 0, 0, 0, 0, 1, 0]), data)
data = np.delete(data, [0, 1, 2], 0)
data = pu.dropData(np.array([0, 1, 0, 0, 0, 0, 0, 1]), data)
data = np.delete(data, [0, 1, 2], 0)
data = pu.dropData(np.array([0, 0, 0, 0, 0, 0, 0, 1]), data)
data = np.delete(data, [0, 1, 2], 0)
data = pu.dropData(np.array([0, 0, 0, 0, 1, 0, 1, 0]), data)
data = np.delete(data, [0, 1, 2], 0)
gc.collect()
#data = np.random.shuffle(data)
print(data[:, 1].shape)
full_X = data[:, 0]
full_Y = data[:, 1]
new_X = full_X[0][np.newaxis, ...]
new_Y = full_Y[0][np.newaxis, ...]
for i in range(full_Y.shape[0] - 1):
new_Y = np.concatenate((new_Y, full_Y[i + 1][np.newaxis, ...]), axis=0)
new_X = np.concatenate((new_X, full_X[i][np.newaxis, ...]), axis=0)
new_data = [np.array(new_X), np.array(new_Y)]
#new_data = pu.qualifyData(new_data)
print(new_data[0].shape)
print(new_data[1].shape)
return new_data
for i in range(full_X.shape[0]):
if i == 0:
addArray = full_X[i]
new_X = addArray[np.newaxis, ...]
if i == 1:
addArray = full_X[i]
print("test")
print(addArray.shape)
print(new_X.shape)
new_X = np.concatenate((new_X, addArray[np.newaxis, ...]), axis=0)
if i > 1:
addArray = full_X[i]
print("test")
print(new_X.shape)
new_X = np.concatenate((new_X, addArray[np.newaxis, ...]), axis=0)
for i in range(full_Y.shape[0]):
if i == 0:
addArray = full_Y[i]
new_Y = addArray[np.newaxis, ...]
if i == 1:
addArray = full_Y[i]
print("test")
print(addArray.shape)
print(new_Y.shape)
new_Y = np.concatenate((new_Y, addArray[np.newaxis, ...]), axis=0)
if i > 1:
addArray = full_Y[i]
print("test")
print(new_Y.shape)
new_Y = np.concatenate((new_Y, addArray[np.newaxis, ...]), axis=0)
def processData(data):
data = pu.dropData(np.array([0, 0, 0, 0, 0, 0, 0, 0]), data)
data = np.delete(data, [0, 1], 0)
data = pu.dropData(np.array([0, 0, 0, 0, 0, 0, 1, 0]), data)
data = np.delete(data, [0, 1], 0)
data = pu.dropData(np.array([0, 0, 0, 0, 0, 0, 0, 1]), data)
data = np.delete(data, [0, 1], 0)
gc.collect()
#data = np.random.shuffle(data)
print(data[:, 1].shape)
full_X = data[:, 0]
full_Y = data[:, 1]
new_X = full_X[0][np.newaxis, ...]
new_Y = full_Y[0][np.newaxis, ...]
for i in range(full_Y.shape[0] - 1):
new_Y = np.concatenate((new_Y, full_Y[i + 1][np.newaxis, ...]), axis=0)
new_X = np.concatenate((new_X, full_X[i][np.newaxis, ...]), axis=0)
split_x = np.array_split(new_X, new_X.shape[0] / 7)
new_X = None
gc.collect()
X = None
for element in split_x:
if element.shape[0] != 7:
continue
if X is None:
X = element[np.newaxis, ...]
else:
print(element.shape)
print(X.shape)
X = np.concatenate((X, element[np.newaxis, ...]), axis=0)
split_y = np.array_split(new_Y, new_Y.shape[0] / 7)
new_Y = None
gc.collect()
y = None
for element in split_y:
if element.shape[0] != 7:
continue
if y is None:
y = element[np.newaxis, ...]
else:
print(element.shape)
print(y.shape)
y = np.concatenate((y, element[np.newaxis, ...]), axis=0)
new_data = [np.array(X), np.array(y)]
#new_data = pu.qualifyData(new_data)
print(new_data[0].shape)
print(new_data[1].shape)
return new_data
def processData(data):
data = pu.dropData(np.array([0, 0, 0, 0, 0, 0, 0, 0]), data)
data = np.delete(data, [0, 1, 2], 0)
data = pu.dropData(np.array([0, 0, 0, 0, 0, 0, 1, 0]), data)
data = np.delete(data, [0, 1, 2], 0)
data = pu.dropData(np.array([0, 1, 0, 0, 0, 0, 0, 1]), data)
data = np.delete(data, [0, 1, 2], 0)
data = pu.dropData(np.array([0, 0, 0, 0, 0, 0, 0, 1]), data)
data = np.delete(data, [0, 1, 2], 0)
data = pu.dropData(np.array([0, 0, 0, 0, 1, 0, 1, 0]), data)
data = np.delete(data, [0, 1, 2], 0)
gc.collect()
#data = np.random.shuffle(data)
print(data[:, 1].shape)
full_X = data[:, 0]
full_Y = data[:, 1]
new_X = full_X[0][np.newaxis, ...]
new_Y = full_Y[0][np.newaxis, ...]
for i in range(full_Y.shape[0]):
try:
if not np.array_equal(full_Y[i], full_Y[i + 1]):
for j in range(3):
index = j - 1
add_Y = full_Y[i + index]
add_X = full_X[i + index]
print(new_Y.shape)
print(add_Y[np.newaxis, ...].shape)
new_Y = np.concatenate((new_Y, add_Y[np.newaxis, ...]),
axis=0)
new_X = np.concatenate((new_X, add_X[np.newaxis, ...]),
axis=0)
else:
print("Skip")
except:
print("not posible")
print(new_X.shape)
new_data = [np.array(new_X), np.array(new_Y)]
new_data = pu.qualifyData(new_data)
print(new_data[0].shape)
print(new_data[1].shape)
return new_data
for i in range(full_X.shape[0]):
if i == 0:
addArray = full_X[i]
new_X = addArray[np.newaxis, ...]
if i == 1:
addArray = full_X[i]
print("test")
print(addArray.shape)
print(new_X.shape)
new_X = np.concatenate((new_X, addArray[np.newaxis, ...]), axis=0)
if i > 1:
addArray = full_X[i]
print("test")
print(new_X.shape)
new_X = np.concatenate((new_X, addArray[np.newaxis, ...]), axis=0)
for i in range(full_Y.shape[0]):
if i == 0:
addArray = full_Y[i]
new_Y = addArray[np.newaxis, ...]
if i == 1:
addArray = full_Y[i]
print("test")
print(addArray.shape)
print(new_Y.shape)
new_Y = np.concatenate((new_Y, addArray[np.newaxis, ...]), axis=0)
if i > 1:
addArray = full_Y[i]
print("test")
print(new_Y.shape)
new_Y = np.concatenate((new_Y, addArray[np.newaxis, ...]), axis=0)