def train_digits(PERCENT=1, EPOCHS=1):
digits = read_data.read_file(fdata='digitdata/trainingimages',
flabel='digitdata/traininglabels',
WIDTH=28,
HEIGHT=28,
type='digits')
num_data = len(digits[0]) #amount of training data
neurons = []
hits = 0
for x in range(10): #create 10 neuron classes
neurons.append(neuron(size=28 * 28))
for epochs in range(EPOCHS):
digits = read_data.read_file(fdata='digitdata/trainingimages',
flabel='digitdata/traininglabels',
WIDTH=28,
HEIGHT=28,
type='digits')
for k in range(int(num_data * PERCENT)):
x = rand.randint(0, len(digits[0]) - 1) #get x as random
features = get_features.features_from_image(
digits[0][x]) #get vector of features
scores = []
for y in neurons: #get score for each class
scores.append(y.score(features))
winnerIndex = scores.index(max(scores))
if winnerIndex != digits[1][x]:
neurons[winnerIndex].weights -= features
neurons[digits[1][x]].weights += features
digits[0].pop(x)
digits[1].pop(x)
return neurons
def train_faces(PERCENT=1, EPOCHS=1):
faces = read_data.read_file(fdata='facedata/facedatatrain',
flabel='facedata/facedatatrainlabels',
WIDTH=60,
HEIGHT=70,
type='faces')
num_data = len(faces[0]) #amount of training
neurons = []
hits = 0
neurons.append(neuron(size=60 * 70))
for epochs in range(EPOCHS):
faces = read_data.read_file(fdata='facedata/facedatatrain',
flabel='facedata/facedatatrainlabels',
WIDTH=60,
HEIGHT=70,
type='faces')
for k in range(int(num_data * PERCENT)):
x = rand.randint(0, len(faces[0]) - 1) #get x as random
features = get_features.features_from_image(
faces[0][x]) #get vector of features
if neurons[0].score(features) < 0 and faces[1][x] == 1:
neurons[0].weights += features
elif neurons[0].score(features) >= 0 and faces[1][x] == 0:
neurons[0].weights -= features
faces[0].pop(x)
faces[1].pop(x)
return neurons
def main():
demo_data = True
config = Config("6_demo.txt")
lr = config.config["learning_rate"]
no_epochs = config.config["no_epochs"]
X_train, y_train = read_file(config.config["training"])
if demo_data:
# Subtract 3 if the demo data is to be used
y_train = np.array([y - 3 for y in y_train])
X_val, y_val = None, None
if "validation" in config.config:
# Read the validation data.
X_val, y_val = read_file(config.config["validation"])
if demo_data:
# Subtract 3 if the demo data is to be used
y_val = np.array([y - 3 for y in y_val])
activation_functions = []
if "activations" in config.config:
activation_functions = config.config["activations"]
loss_type = config.config["loss_type"]
l2_regularization_factor = config.config["L2_regularization"]
layers = config.config["layers"]
# Insert the number of features as the number of nodes in the first layer.
layers.insert(0, X_train.shape[1])
if loss_type == "cross_entropy":
# If cross_entropy is used then we need to use softmax for the last layer.
n_classes = get_num_of_classes(y_train)
activation_functions.append("softmax")
# Append n_classes as the number of nodes in the last layer.
layers.append(n_classes)
y_train = one_hot(y_train, classes=n_classes)
if y_val is not None:
y_val = one_hot(y_val, classes=n_classes)
else: # L2
activation_functions.append(
"linear") # TODO: Typisk linear, kan være relu og.
# Here we append 1 node at the last layer.
layers.append(1)
network = Network(X_train,
y_train,
layers,
loss_type,
activation_functions,
lr,
X_val=X_val,
y_val=y_val,
regularization_factor=l2_regularization_factor,
no_epochs=no_epochs)
assert len(layers) == len(activation_functions) + 1
network.train()
def extract_data(self, path):
#根据指定路径读取出图片、标签和类别数
imgs, labels, counter = read_file(path)
#将数据集打乱随机分组
X_train, X_test, y_train, y_test = train_test_split(
imgs, labels, test_size=0.2, random_state=random.randint(0, 100))
#重新格式化和标准化
# 本案例是基于theano的，如果基于tensorflow的backend需要进行修改
#X_train = X_train.reshape(X_train.shape[0], 1, self.img_size, self.img_size)/255.0 #参数：shap[0]读取矩阵第一维的长度，变为一列
#X_test = X_test.reshape(X_test.shape[0], 1, self.img_size, self.img_size) / 255.0
#‘channels_last’模式下，输入形如（samples，rows，cols，channels）的4D张量
X_train = X_train.reshape(X_train.shape[0], self.img_size,
self.img_size,
1) / 255.0 # 参数：shap[0]读取矩阵第一维的长度，变为一列
X_test = X_test.reshape(X_test.shape[0], self.img_size, self.img_size,
1) / 255.0
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
#将labels转成 binary class matrices
Y_train = np_utils.to_categorical(y_train, num_classes=counter)
Y_test = np_utils.to_categorical(y_test, num_classes=counter)
#将格式化后的数据赋值给类的属性上
self.X_train = X_train
self.X_test = X_test
self.Y_train = Y_train
self.Y_test = Y_test
self.num_classes = counter
def train_faces(PERCENTAGE=1):
faces = read_data.read_file(fdata='facedata/facedatatrain',
flabel='facedata/facedatatrainlabels',
WIDTH=60,
HEIGHT=70,
type='faces')
num_data = len(faces[0]) #amount of training data
features = get_features.advanced_features_from_image(faces[0][0])
face_class = label()
face_class.features = np.zeros(len(features))
not_face_class = label()
not_face_class.features = np.zeros(len(features))
'''
get frequency of feature values for each feature in training set
'''
for k in range(int(num_data *
PERCENTAGE)): # for each training data number
x = rand.randint(0, len(faces[0]) - 1) #get x as random index
features = get_features.advanced_features_from_image(
faces[0][x]) #get vector of features
if faces[1][x] == 0:
not_face_class.frequency += 1
not_face_class.features += features
elif faces[1][x] == 1:
face_class.frequency += 1
face_class.features += features
faces[0].pop(x)
faces[1].pop(x)
return face_class, not_face_class, num_data
def extract_data(self, path):
#根据指定路径读取出图片、标签和类别数
imgs, labels, counter = read_file(path)
#将数据集打乱随机分组
X_train, X_test, y_train, y_test = train_test_split(
imgs, labels, test_size=0.2, random_state=random.randint(0, 100))
#重新格式化和标准化
X_train = X_train.reshape(
X_train.shape[0], self.img_size, self.img_size,
1) / 255.0 #将x_train.shape[0]（垂直尺寸）调整，变成128*128*1再除以255.0
X_test = X_test.reshape(X_test.shape[0], self.img_size, self.img_size,
1) / 255.0
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
#将labels转成 binary class matrices
Y_train = np_utils.to_categorical(y_train, num_classes=counter)
Y_test = np_utils.to_categorical(y_test, num_classes=counter)
#将格式化后的数据赋值给类的属性上
self.X_train = X_train
self.X_test = X_test
self.Y_train = Y_train
self.Y_test = Y_test
self.num_classes = counter
def extract_data(self, path):
# 根据指定路径读取出图片、标签和类别数
imgs, labels, counter = read_file(path)
# 将数据集打乱随机分组
X_train, X_test, y_train, y_test = train_test_split(imgs, labels, test_size=0.2,
random_state=random.randint(0, 100))
# 重新格式化和标准化
# 本案例是基于thano的，如果基于tensorflow的backend需要进行修改
X_train = X_train.reshape(X_train.shape[0], 1, self.imgX_size, self.imgY_size) / 255.0
X_test = X_test.reshape(X_test.shape[0], 1, self.imgX_size, self.imgY_size) / 255.0
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# 将labels转成 binary class matrices
Y_train = np_utils.to_categorical(y_train, num_classes=counter)
Y_test = np_utils.to_categorical(y_test, num_classes=counter)
# 将格式化后的数据赋值给类的属性上
self.X_train = X_train
self.X_test = X_test
self.Y_train = Y_train
self.Y_test = Y_test
self.num_classes = counter
def perceptron_deploy(digit=1, PERCENTAGE=1, digits=True):
if digits:
data = read_data.read_file(fdata='digitdata/testimages',
flabel='digitdata/testlabels',
WIDTH=28,
HEIGHT=28,
type='digits')
else:
data = read_data.read_file(fdata='facedata/facedatatest',
flabel='facedata/facedatatestlabels',
WIDTH=60,
HEIGHT=70,
type='faces')
num_data = len(data[1]) #amount of training data
neurons = []
if digits:
neurons = perceptron.train_digits(PERCENT=PERCENTAGE, EPOCHS=1)
else:
neurons = perceptron.train_faces(PERCENT=PERCENTAGE, EPOCHS=1)
print("Trained Model!")
hits = 0
num = 0
if digits:
while True:
num = rand.randint(0, len(data[0]) - 1)
if digit == data[1][num]:
print("Found Digit to Guess!")
break
else:
num = rand.randint(0, len(data[0]) - 1)
features = get_features.features_from_image(
data[0][num]) #get vector of features
scores = []
for y in neurons:
scores.append(y.score(features))
if digits:
winnerIndex = scores.index(max(scores))
print("Predicted the digit: %s" % winnerIndex)
else:
if scores[0] < 0:
print("Not Face!")
else:
print("Is Face!")
plt.imshow(data[0][num])
plt.show()
def main():
lookback_window = 50
raw_time_sequences = []
cutoffs = []
for input_filename in sorted(glob('../output/*_VIEW_13.json'))[0:20]:
d = read_file(input_filename)
d.drop('Annual', axis=1, inplace=True)
d_train = d.head(len(d) - HOW_MANY_YEARS_IN_TEST)
cutoff_train_test_index = len(d_train.values.flatten())
time_sequence = d.values.flatten()
time_sequence = time_sequence[~np.isnan(time_sequence)]
time_sequence = np.log(
time_sequence +
1e-6) # simple normalization. could be per station.
raw_time_sequences.append(time_sequence)
cutoffs.append(cutoff_train_test_index)
mean = np.mean(np.concatenate(raw_time_sequences))
std = np.std(np.concatenate(raw_time_sequences))
scale = 10
x_train, y_train, x_test, y_test = [], [], [], []
for time_sequence, cutoff_train_test_index in zip(raw_time_sequences,
cutoffs):
# normalization
time_sequence = (time_sequence - mean) / std / scale
for i in range(lookback_window, len(time_sequence)):
model_input_slice = time_sequence[i - lookback_window:i]
if i < cutoff_train_test_index:
x_train.append(model_input_slice)
y_train.append(time_sequence[i])
else:
x_test.append(model_input_slice)
y_test.append(time_sequence[i])
x_train = np.expand_dims(x_train, axis=-1)
y_train = np.expand_dims(y_train, axis=-1)
x_test = np.expand_dims(x_test, axis=-1)
y_test = np.expand_dims(y_test, axis=-1)
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)
m = Sequential()
m.add(LSTM(128, input_shape=x_train.shape[1:]))
m.add(Dense(128, activation='relu'))
m.add(Dense(1, activation='linear'))
opt = RMSprop(lr=1e-4)
m.compile(loss='mae', optimizer=opt)
for epoch in range(100):
p = np.exp(m.predict(x_test) * scale * std + mean)
t = np.exp(y_test * scale * std + mean)
error = np.mean(np.abs(p - t))
print(epoch, error)
m.fit(x_train,
y_train,
shuffle=True,
validation_data=(x_test, y_test),
epochs=1,
batch_size=256,
verbose=0)
def read_file_data(self, path):
try:
art, use_depth, coord_transform, polar_shift, name = read_file(
path)
self.read_art_params(art, use_depth, coord_transform, polar_shift,
name)
except:
print("Failed to read file " + path)
def load_data(self): pwd = os.path.dirname(os.path.realpath(__file__)) self.gyroscope = np.mat(read_file(pwd+'/FilterTestData/Gyroscope.txt')) self.accelerometer = np.mat(read_file(pwd+'/FilterTestData/Accelerometer.txt')) self.magnetometer = np.mat(read_file(pwd+'/FilterTestData/Magnetometer.txt')) self.time = np.mat(read_file(pwd+'/FilterTestData/Time.txt')) self.quaternion_madgwick_imu = np.roll( np.mat( read_file(pwd+'/FilterTestData/quaternion_madgwick_imu.txt') ), -1) self.quaternion_madgwick_marg = np.roll( np.mat( read_file(pwd+'/FilterTestData/quaternion_madgwick_marg.txt') ), -1 ) self.quaternion_mahoney_imu = np.roll( np.mat( read_file(pwd+'/FilterTestData/quaternion_mahoney_imu.txt') ), -1 ) self.quaternion_mahoney_marg = np.roll( np.mat( read_file(pwd+'/FilterTestData/quaternion_mahoney_marg.txt') ), -1 )
def deploy_model(PERCENT,
EPOCHS,
digits=True): #set digits to false to train faces
if digits:
data = read_data.read_file(fdata='digitdata/testimages',
flabel='digitdata/testlabels',
WIDTH=28,
HEIGHT=28,
type='digits')
else:
data = read_data.read_file(fdata='facedata/facedatatest',
flabel='facedata/facedatatestlabels',
WIDTH=60,
HEIGHT=70,
type='faces')
num_data = len(data[1]) #amount of training data
neurons = []
if digits:
neurons = train_digits(PERCENT=PERCENT, EPOCHS=EPOCHS)
else:
neurons = train_faces(PERCENT=PERCENT, EPOCHS=EPOCHS)
hits = 0
for x in range(num_data):
features = get_features.features_from_image(
data[0][x]) #get vector of features
scores = []
for y in neurons:
scores.append(y.score(features))
if digits:
winnerIndex = scores.index(max(scores))
if winnerIndex == data[1][x]:
hits += 1
else:
if scores[0] < 0 and data[1][x] == 0:
hits += 1
elif scores[0] >= 0 and data[1][x] == 1:
hits += 1
return hits / num_data
def test_onBatch(path):
model = Model()
model.load()
index = 0
img_list, label_lsit, counter = read_file(path)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
print(labbel[picType], prob)
else:
print("invaild person")
return index
def test_onBatch(path):
model = Model()
model.load()
index = 0
img_list, label_lsit, counter = read_file(path)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
name_list = read_name_list('./image/trainfaces')
print(name_list[picType])
else:
print(" Don't know this person")
return index
def test_onBatch(path):
model= Model()
model.load()
index = 0
img_list, label_lsit, counter = read_file(path)
for img in img_list:
picType,prob = model.predict(img)
if picType != -1:
index += 1
name_list = read_name_list('/Users/gaoxingyun/Documents/uw/courses/Sp19/EE576_CV/project/faceRecognition/dataset')
print (name_list[picType])
else:
print (" Don't know this person")
return index
def test_onBatch(path):
model = Model()
model.load()
index = 0
img_list, label_lsit, counter = read_file(path)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
name_list = read_name_list('D:\myProject\pictures\dataset')
print name_list[picType]
else:
print " Don't know this person"
return index
def infrence_model(PERCENTAGE=1):
SMOOTHER = 1
label_obj, num_data = train_digits(PERCENTAGE=PERCENTAGE)
digits = read_data.read_file(fdata='digitdata/testimages',
flabel='digitdata/testlabels',
WIDTH=28,
HEIGHT=28,
type='digits')
predictions = [] #outputs from bayes classifier
for x in range(len(digits[0])):
features = get_features.features_from_image(
digits[0][x]) #get array of features
maxls = []
cur_guess = None
for y in range(
10): #get prob of each label and choose highest as answer
p_y = math.log(
(label_obj[y].frequency) / int(num_data * PERCENTAGE))
likelihood = 0
for feats in range(len(features)):
if features[feats] == 0:
likelihood += math.log(
(label_obj[y].v0[feats] + SMOOTHER) /
(label_obj[y].frequency + label_obj[y].v0[feats]) *
SMOOTHER)
elif features[feats] == 1:
likelihood += math.log(
(label_obj[y].v1[feats] + SMOOTHER) /
(label_obj[y].frequency + label_obj[y].v1[feats]) *
SMOOTHER)
elif features[feats] == 2:
likelihood += math.log(
(label_obj[y].v2[feats] + SMOOTHER) /
(label_obj[y].frequency + label_obj[y].v2[feats]) *
SMOOTHER)
likelihood = likelihood + p_y
maxls.append(likelihood)
predictions.append(maxls.index(max(maxls)))
hits = 0
for x in range(len(digits[1])):
if predictions[x] == digits[1][x]:
hits += 1
accuracy = hits / len(digits[1])
return accuracy
def test_onBatch(path):
model = Model()
model.load()
index = 0
img_list, label_lsit, counter = read_file(path)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
# name_list = read_name_list('D:\myProject\pictures\dataset')
name_list = read_name_list(
r'D:/my_laboratory/face_detection20180516/dataset')
print(name_list[picType])
else:
print(" Don't know this person")
return index
def test_onBatch(path):
model = Model()
model.load()
index = 0
img_list, label_lsit, counter = read_file(path)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
name_list = read_name_list(
'C:\\Users\\jimmychen\\Desktop\\chernger\\chernger_faceRecognition\\dataset'
)
print(name_list[picType])
else:
print("Don't know this person")
return index
def bayes_digit(digit=1, PERCENTAGE=1):
SMOOTHER = 1
label_obj, num_data = naive_bayes_digits.train_digits(PERCENTAGE=1)
print("Trained Model!")
digits = read_data.read_file(fdata='digitdata/testimages',
flabel='digitdata/testlabels',
WIDTH=28,
HEIGHT=28,
type='digits')
num = 0
while True:
num = rand.randint(0, len(digits[0]) - 1)
if digit == digits[1][num]:
break
print("Found Digit to Guess!")
features = get_features.features_from_image(
digits[0][num]) #get array of features
maxls = []
for y in range(10): #get prob of each label and choose highest as answer
p_y = math.log((label_obj[y].frequency) / int(num_data) * PERCENTAGE)
likelihood = 0
for feats in range(len(features)):
if features[feats] == 0:
likelihood += math.log(
(label_obj[y].v0[feats] + SMOOTHER) /
(label_obj[y].frequency + label_obj[y].v0[feats]) *
SMOOTHER)
elif features[feats] == 1:
likelihood += math.log(
(label_obj[y].v1[feats] + SMOOTHER) /
(label_obj[y].frequency + label_obj[y].v1[feats]) *
SMOOTHER)
elif features[feats] == 2:
likelihood += math.log(
(label_obj[y].v2[feats] + SMOOTHER) /
(label_obj[y].frequency + label_obj[y].v2[feats]) *
SMOOTHER)
likelihood = likelihood + p_y
maxls.append(likelihood)
prediction = maxls.index(max(maxls))
print("Predicted the digit: %s" % prediction)
plt.imshow(digits[0][num])
plt.show()
def data_handle(path):
imgs, labels, counter = read_file(path)
imgs -= MEAN_PIXEL
X_train, X_test, y_train, y_test = train_test_split(
imgs, labels, test_size=0.2, random_state=random.randint(0, 100))
X_train = X_train.reshape(X_train.shape[0], img_size, img_size, 3) / 255.0
X_test = X_test.reshape(X_test.shape[0], img_size, img_size, 3) / 255.0
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# 多目标的label:one hot coding
Y_train = np_utils.to_categorical(y_train)
Y_test = np_utils.to_categorical(y_test)
num_classes = counter
print('handle successful,', X_train.shape[0])
return X_train, X_test, Y_train, Y_test, num_classes
def get_feature_objects(filename: str,
max_items: int) -> (Dict[str, Features]):
result_filename = filename + "results_stats.bin"
if os.path.isfile(result_filename):
with open(result_filename, "rb") as file:
return pickle.load(file)
else:
c = ComputeFeatures()
features = dict()
for data in read_file(filename, max_items, True):
if data.playerId not in features:
features[data.playerId] = []
features[data.playerId].append(c.compute_features(data))
with open(result_filename, "wb") as file:
pickle.dump(features, file)
return features
def extract_data(self, path):
#Read the number of pictures, tags, and categories based on the specified path
imgs, labels, counter = read_file(path)
print("Output tag")
print(labels)
#Dataset random grouping
X_train, X_test, y_train, y_test = train_test_split(
imgs, labels, test_size=0.4, random_state=random.randint(0, 100))
print("Output training mark and training set length")
print(y_train)
print(len(X_train))
print(X_train[1])
print("Test length and test set tag")
print(len(X_test))
print(y_test)
print("Output and")
print(counter)
#reformatting and standardization
# This case is based on thano, if the backend based on tensorflow needs to be modified
print(X_train.shape)
X_train = X_train.reshape(X_train.shape[0], 480, 640, 1)
X_test = X_test.reshape(X_test.shape[0], 480, 640, 1)
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
print(X_train[1])
#labels into binary class matrices
Y_train = np_utils.to_categorical(y_train, num_classes=counter)
Y_test = np_utils.to_categorical(y_test, num_classes=counter)
print(Y_train)
# Assign the formatted data to the properties of the class
self.X_train = X_train
self.X_test = X_test
self.Y_train = Y_train
self.Y_test = Y_test
self.num_classes = counter
def extract_data(self, path):
imgs, labels, counter = read_file(path)
X_train, X_test, y_train, y_test = train_test_split(
imgs, labels, test_size=0.2, random_state=random.randint(0, 100))
X_train = X_train.reshape(X_train.shape[0], 1, self.img_size,
self.img_size) / 255.0
X_test = X_test.reshape(X_test.shape[0], 1, self.img_size,
self.img_size) / 255.0
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
Y_train = np_utils.to_categorical(y_train, num_classes=counter)
Y_test = np_utils.to_categorical(y_test, num_classes=counter)
self.X_train = X_train
self.X_test = X_test
self.Y_train = Y_train
self.Y_test = Y_test
self.num_classes = counter
def test_onBatch(path):
model = Model()
model.load()
index = 0
img_list, label_list, counter = read_file(path)
# img_list = img_list.reshape(img_list.shape[0], 174, 212, 1)
# print(img_list.shape[0:])
# img_list = img_list.astype('float32')/255
# Label_list = np_utils.to_categorical(label_list, num_classes=counter)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
name_list = read_name_list('G:/desktop/myProject/pictures/test')
print(name_list)
print(name_list[picType])
else:
print(" Don't know this person")
return index
def extract_data(self, path):
#根据指定路径读取出图片、标签和类别数
imgs, labels, counter = read_file(path)
print("输出标记")
print(labels)
#将数据集打乱随机分组
X_train, X_test, y_train, y_test = train_test_split(
imgs, labels, test_size=0.4, random_state=random.randint(0, 100))
print("输出训练标记和训练集长度")
print(y_train)
print(len(X_train))
print(X_train[1])
print("测试长度和测试集标记")
print(len(X_test))
print(y_test)
print("输出和")
print(counter)
#重新格式化和标准化
# 本案例是基于thano的，如果基于tensorflow的backend需要进行修改
X_train = X_train.reshape(X_train.shape[0], 174, 212, 1)
X_test = X_test.reshape(X_test.shape[0], 174, 212, 1)
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
print(X_train[1])
#将labels转成 binary class matrices
Y_train = np_utils.to_categorical(y_train, num_classes=counter)
Y_test = np_utils.to_categorical(y_test, num_classes=counter)
print(Y_train)
#将格式化后的数据赋值给类的属性上
self.X_train = X_train
self.X_test = X_test
self.Y_train = Y_train
self.Y_test = Y_test
self.num_classes = counter
def get_features(filename: str,
max_items: int,
label_present=True) -> (List[str], List[List]):
result_filename = filename + "results.bin"
if os.path.isfile(result_filename):
with open(result_filename, "rb") as file:
data = pickle.load(file)
return data["labels"], data["features"]
else:
c = ComputeFeatures()
features = []
labels = []
for data in read_file(filename, max_items, label_present):
if label_present:
labels.append(data.playerId)
features.append(c.compute_features(data).to_array())
with open(result_filename, "wb") as file:
pickle.dump({"labels": labels, "features": features}, file)
return labels, features
def main(file_loc):
global explode, pos, neg, emos, counter, handler, span, dates, words_total
if ('.txt' not in file_loc):
file_txt = file_loc[:-3] + 'txt'
if not os.path.exists(file_txt):
data_set = read_data_set(file_loc)
file = open(file_txt, 'w')
for month in data_set:
file.write(month.to_string() + '\n')
file.close()
file_loc = file_txt
(data_set, words_total) = read_file(file_loc)
(counter, dates) = sum_data_set(data_set)
set_emos(data_set)
pos = counter.pop(0)
neg = counter.pop(0)
(plot, stacked) = make_flower()
handler = FlowerEventHandler(plot)
span = SpanSelector(stacked,
onselect,
'horizontal',
useblit=True,
rectprops=dict(alpha=0.5, facecolor='red'))
subplots_adjust(left=0.0,
bottom=0.1,
right=0.95,
top=0.91,
wspace=0.04,
hspace=0.22)
plt.show(block=True)
def main2():
global explode, pos, neg, emos, counter, handler, span, dates, words_total
(data_set, words_total) = read_file('../data/article_compact.txt')
(counter, dates) = sum_data_set(data_set)
set_emos(data_set)
pos = counter.pop(0)
neg = counter.pop(0)
(plot, stacked) = make_flower()
handler = FlowerEventHandler(plot)
span = SpanSelector(stacked,
onselect,
'horizontal',
useblit=True,
rectprops=dict(alpha=0.5, facecolor='red'))
subplots_adjust(left=0.0,
bottom=0.1,
right=0.95,
top=0.91,
wspace=0.04,
hspace=0.22)
plt.show(block=True)
def train_digits(PERCENTAGE=1):
digits = read_data.read_file(fdata='digitdata/trainingimages',
flabel='digitdata/traininglabels',
WIDTH=28,
HEIGHT=28,
type='digits')
num_data = len(digits[0]) #amount of training data
label_obj = []
features = get_features.features_from_image(digits[0][0])
for x in range(10): #create 10 label objects for each class
lbl = label()
lbl.v0 = np.ones(len(features))
lbl.v1 = np.ones(len(features))
lbl.v2 = np.ones(len(features))
label_obj.append(lbl)
'''
get frequency of feature values for each feature in training set
'''
for k in range(int(num_data *
PERCENTAGE)): # for each training data number
x = rand.randint(0, len(digits[0]) - 1) #get x as random index
features = get_features.features_from_image(
digits[0][x]) #get vector of features
label_obj[digits[1][x]].frequency += 1
for y in range(len(features)):
if features[y] == 0:
label_obj[digits[1][x]].v0[y] += 1
elif features[y] == 1:
label_obj[digits[1][x]].v1[y] += 1
elif features[y] == 2:
label_obj[digits[1][x]].v2[y] += 1
digits[0].pop(x)
digits[1].pop(x)
return label_obj, num_data
' to use when collecting training experiences.')
args = parser.parse_args()
continue_from_file = args.continue_from_file
model_name = args.model_name
simulator_type = args.simulator_type
all_configs = load(open(config_filename, 'r'), Loader=yaml.FullLoader)
config = all_configs[model_name]
device = get_device()
# Find the input size, hidden dim sizes, and output size
env_name = config['env_name']
# env = gym.make(env_name)
data = read_file('./data/train.csv')
embeddings = Embeddings(read_embeddings('./data/embeddings.csv'))
env = gym.make(env_name,
data=data,
embeddings=embeddings,
alpha=0.5,
gamma=0.9,
fixed_length=True,
trajectory_length=5)
action_space = env.action_space
observation_space = env.observation_space
policy_hidden_dims = config['policy_hidden_dims']
vf_hidden_dims = config['vf_hidden_dims']
vf_args = (observation_space.shape[0] + 1, vf_hidden_dims, 1)
# Initialize the policy
from read_data import read_file, get_ratexuser, get_movie_genres from similarity import recommend from user_personalization import print_question data, labels = read_file() # Get all the movie titles just one time movies, genres = get_movie_genres(data, labels) # separate the users by Id and then take just the ranking for each movie data_dicts, user_ids = get_ratexuser(data) print_question(data, labels, user_ids, data_dicts)
import read_data as rd import vectorization as vt import LSTM_Class as lstmcl import time as t import datetime import math import matplotlib.pyplot as plt tic = t.time() example = 'example_2015_12_2016_01' # read training data file_name = 'TrainData.txt' file_dir = 'txt_files\examples\\' + example + '\\' + file_name req_contents_tr = rd.read_file(file_dir) # read test data file_name = 'TestData.txt' file_dir = 'txt_files\examples\\' + example + '\\' + file_name req_contents_ts = rd.read_file(file_dir) # decode traing & test data req_data_tr = rd.decode_req_data(req_contents_tr) req_data_ts = rd.decode_req_data(req_contents_ts) # read input file file_name = "input.txt" file_dir = "txt_files\examples\\" + example + '\\' + file_name input_file_contents = rd.read_file(file_dir)