def main(K=6):
# set seed
np.random.seed(1)
# set numpy print options (pretty print matrix)
np.set_printoptions(precision=4, suppress=True, floatmode='fixed')
# load Data
data = read_csv()
# preprocess
X = normalize_data([d[0] for d in data])
Y = normalize_data([d[1] for d in data])
data = np.array([X, Y]).transpose()
model = VBGMM(data, K)
# run mixGaussBayesFit
posterior, loglikHist = model.fit()
model.plot_posterior_alpha()
# plot likelihood
plt.plot(loglikHist, '-', marker='*', lw=3)
plt.yticks(np.arange(-1100, -601, 50))
plt.xlim([0, 100])
plt.xlabel('iterations')
plt.ylabel('lower bound on log marginal likelihood')
plt.title('variational Bayes objective for GMM on old faithful data')
plt.show()
def test_digits(model, digits, labels, ensemble_size, reshape_fun):
steps_results = {'c_error': {}, 'entropy': {}}
dnum = 80
for i in range(1, 101):
dless, dmore = salt_and_pepper(digits, i * dnum)
d = utils.normalize_data(reshape_fun(dmore))
entropy = ann.test_model(model, [d] * ensemble_size,
labels,
metric='entropy')
c_error = ann.test_model(model, [d] * ensemble_size,
labels,
metric='c_error')
steps_results['entropy'][i] = entropy
steps_results['c_error'][i] = c_error
d = utils.normalize_data(reshape_fun(dless))
entropy = ann.test_model(model, [d] * ensemble_size,
labels,
metric='entropy')
c_error = ann.test_model(model, [d] * ensemble_size,
labels,
metric='c_error')
steps_results['entropy'][-1 * i] = entropy
steps_results['c_error'][-1 * i] = c_error
return steps_results
def iris_dataset_classification():
# Set the seed to make result reproducible
np.random.seed(50)
# Loads iris dataset
iris = datasets.load_iris()
train_data, test_data, train_labels, test_labels = split_and_shuffle_train_test(
iris.data, iris.target)
# Does one hot encoding and transposes labels for use in network
encoding, train_labels = one_hot_encoding(train_labels)
train_labels = train_labels.T
# Normalizes data
train_data = normalize_data(train_data).T
# Creates a range in which we will test different hidden layer number of neurons
hidden_layer_neurons = range(200, 600, 30)
# Here we store the precision for each hidden layer composition
total_precision = []
for neuron in hidden_layer_neurons:
network = TwoLayerNetwork(4, neuron, 3)
network.train(train_data, train_labels, 10000, 0.001)
network.plot_training_cost()
predictions = network.predict(normalize_data(test_data).T)
cm = ConfusionMatrix(predictions, test_labels)
cm.matrix_summary()
total_precision.append(cm.total_precision())
plot_precision_vs_number_neurons(hidden_layer_neurons, total_precision)
def test(self):
# load dataset
file1_va = h5py.File('./data_da/data1750.h5', 'r')
train_data = file1_va['data1750_x'][:]
train_data = train_data.reshape(len(train_data), 1, 4096, 1)
train_label = file1_va['data1750_y'][:]
file2_va = h5py.File('./data_da/data1730.h5', 'r')
test_data = file2_va['data1730_x'][:]
test_data = test_data.reshape(len(test_data), 1, 4096, 1)
test_label = file2_va['data1730_y'][:]
train_data = utils.normalize_data(train_data, 'std')
test_data = utils.normalize_data(test_data, 'std')
ms = self.build_M()
ms.load_weights('./net_weights/best_mt.hdf5')
ms.compile(optimizer=keras.optimizers.Adam(), loss='mse')
c = self.build_C()
c.load_weights('./net_weights/best_c.hdf5')
c.compile(optimizer=keras.optimizers.Adam(),
loss='categorical_crossentropy')
train_fea = ms.predict(train_data)
test_fea = ms.predict(test_data)
test_pre = c.predict(test_fea)
sio.savemat(
'gan_fea_pca4test_BA_7.mat', {
'train_fea': train_fea,
'train_label': train_label,
'test_fea': test_fea,
'test_label': test_label,
'test_pre': test_pre
})
'''layer_name = 'conv1'
def extract_normalize_images(self):
x_train_valid = self.data_df.iloc[:, 1:].values.reshape(
-1, self.img_h, self.img_w, 1) # (42000,28,28,1) array
x_train_valid = x_train_valid.astype(
np.float) # convert from int64 to float32
x_train_valid = utils.normalize_data(x_train_valid)
x_test = self.test_df.iloc[:, 0:].values.reshape(
-1, self.img_h, self.img_w, 1) # (28000,28,28,1) array
x_test = x_test.astype(np.float)
x_test = utils.normalize_data(x_test)
image_size = 784
# extract image labels
y_train_valid_labels = self.data_df.iloc[:,
0].values # (42000,1) array
labels_count = np.unique(y_train_valid_labels).shape[0]
# number of different labels = 10
#plot some images and labels
#plt.figure(figsize=(15,9))
#for i in range(50):
# plt.subplot(5,10,1+i)
# plt.title(y_train_valid_labels[i])
# plt.imshow(x_train_valid[i].reshape(28,28), cmap=cm.binary)
# labels in one hot representation
y_train_valid = utils.dense_to_one_hot(y_train_valid_labels,
labels_count).astype(np.uint8)
return (x_train_valid, y_train_valid, x_test)
def get_testing_batch():
while True:
for sequence in test_loader:
sequence_0, sequence_match = sequence
batch_0 = utils.normalize_data(opt, dtype, sequence_0)
batch_match = []
for i in range(5):
batch_match.append(utils.normalize_data(opt, dtype, sequence_match[i]))
yield batch_0, batch_match
def get_batch_generator(data_loader):
while True:
for sequence in data_loader:
if not opt.use_action:
batch = utils.normalize_data(opt, dtype, sequence)
yield batch
else:
images, actions = sequence
images = utils.normalize_data(opt, dtype, images)
actions = utils.sequence_input(actions.transpose_(0, 1), dtype)
yield images, actions
def normalize(self, model=settings.MODEL):
if model == "mlp" or model == "test":
self.images_outer_flat = normalize_data(self.images_outer_flat)
self.images_inner_flat = normalize_data(self.images_inner_flat)
elif model == "conv_mlp":
self.images_outer2d = normalize_data(self.images_outer2d)
self.images_inner_flat = normalize_data(self.images_inner_flat)
elif model == "conv_deconv" or model == "lasagne_conv_deconv":
self.images_outer2d = normalize_data(self.images_outer2d)
self.images_inner2d = normalize_data(self.images_inner2d)
elif model == "dcgan" or model == "wgan" or model == "lsgan":
self.images = normalize_data(self.images)
self.images_inner2d = normalize_data(self.images_inner2d)
elif model == "vgg16":
self.images_outer2d = self.images_outer2d.astype('float32')
self.images_inner2d = self.images_inner2d.astype('float32')
for col_index, subtract in enumerate([103.939, 116.779, 123.68]):
self.images_outer2d[:, col_index, :, :] -= subtract
self.images_inner2d[:, col_index, :, :] -= subtract
r, g, b = self.images_outer2d[:,
0, :, :], self.images_outer2d[:,
1, :, :], self.images_outer2d[:,
2, :, :]
self.images_outer2d[:,
0, :, :], self.images_outer2d[:,
1, :, :], self.images_outer2d[:,
2, :, :] = b, g, r
r, g, b = self.images_inner2d[:,
0, :, :], self.images_inner2d[:,
1, :, :], self.images_inner2d[:,
2, :, :]
self.images_inner2d[:,
0, :, :], self.images_inner2d[:,
1, :, :], self.images_inner2d[:,
2, :, :] = b, g, r
def predict_feature_map(self):
input_data = pd.read_csv(self.annotations_path)
n_samples = input_data.shape[0]
self.feature_map = np.empty((n_samples, ) +
self.model.layers[-1].output.shape[1:])
for i in range(0, n_samples // self.batch_size):
# TODO: get dtype from model
images = np.empty((self.batch_size, ) + self.get_input_shape(),
dtype=np.int)
for j, image_path in enumerate(
input_data.iloc[i * self.batch_size:(i + 1) *
self.batch_size]["image_path"]):
image = PIL.Image.open(
os.path.join(self.image_path, image_path))
if len(np.array(image).shape) != 3:
rgbimg = PIL.Image.new("RGB", image.size)
rgbimg.paste(image)
image = rgbimg
image = image.resize(self.get_input_shape()[:-1])
image = np.array(image)
images[j] = image
self.feature_map[i * self.batch_size:(i + 1) *
self.batch_size] = self.model(images)
self.feature_map = normalize_data(self.feature_map)
def get_testing_batch():
while True:
for image_seq, action_seq in test_loader:
image_seq = utils.normalize_data(opt, dtype, image_seq)
action_seq = utils.sequence_input(action_seq.transpose_(0, 1),
dtype)
yield image_seq, action_seq
def main():
# cleaning
utils.remove_all_files_inside_folder('./results/')
utils.remove_all_files_inside_folder('./training_checkpoints/')
# prepare dataset
(train_images, _), (_, _) = utils.get_fmnist_data()
train_dataset = utils.normalize_data(train_images)
# create models
generator = utils.Generator()
discriminator = utils.Discriminator()
# Defun gives 10 secs/epoch performance boost
generator.call = tf.contrib.eager.defun(generator.call)
discriminator.call = tf.contrib.eager.defun(discriminator.call)
# training helpers
checkpoint = utils.setup_checkpoint(generator, discriminator)
random_vector = utils.generate_constant_random_vector(
NOISE_DIM, NUM_EXAMPLES_TO_GENERATE)
# training
history = utils.train(dataset=train_dataset, epochs=EPOCHS, noise_dim=NOISE_DIM, generator=generator,
discriminator=discriminator, checkpoint=checkpoint, random_vector=random_vector)
# reporting
generator.summary()
discriminator.summary()
utils.plot_loss(history)
utils.create_gif()
def test_digits(model, digits, labels, ensemble_size, reshape_fun):
steps_results = {'c_error': {}, 'entropy': {}}
dnum = 200
pb = ProgressBar(total=100,
prefix='Sim trial progress',
length=25,
fill='=',
zfill='_')
for i in range(1, 101):
dnoice = salt_and_pepper(digits, i * dnum)
d = utils.normalize_data(reshape_fun(dnoice))
entropy = ann.test_model(model, [d] * ensemble_size,
labels,
metric='entropy')
c_error = ann.test_model(model, [d] * ensemble_size,
labels,
metric='c_error')
steps_results['entropy'][i] = entropy
steps_results['c_error'][i] = c_error
pb.print_progress_bar(i)
return steps_results
def pre_process_data(self):
in_features = self.data_config['in_features']
out_features = self.data_config['out_features']
data_obj = DATA(freq=self.data_config['freq'])
all_data = data_obj.get_df()
# copying required data
df = all_data[in_features].copy()
for out in out_features:
df[out] = all_data[out].copy()
if self.verbosity > 0:
print('shape of whole dataset', df.shape)
# assuming that pandas will add the 'datetime' column as last column. This columns will only be used to keep
# track of indices of train and test data.
df['datetime'] = list(
map(int, np.array(df.index.strftime('%Y%m%d%H%M'))))
# columns containing target data (may) have nan values because missing values are represented by nans
# so convert those nans 0s. This is with a big assumption that the actual target data does not contain 0s.
# they are converted to zeros because in LSTM and at other places as well we will select the data based on mask
# such as values>0.0 and if target data has zeros, we can not do this.
dataset = nan_to_num(df.values,
len(out_features) + 1,
replace_with=0.0)
if self.data_config['normalize']:
dataset, self.scalers['all'] = normalize_data(
dataset, df.columns, 1)
return dataset # , scalers
def patch(self, id):
try:
measurement = Measurement.objects(id=id).first()
if measurement is not None:
if get_formatted_date(
measurement.created) != get_formatted_date(
get_today_date()):
raise BadRequest(
f'Cannot update a measurement for {get_formatted_date(measurement.created)}'
)
data = self.reqparse.parse_args()
data = normalize_data(data)
measurement.update(**data)
measurement.reload()
return measurement.to_dict(), 200
abort(404, message=f'Measurement ID={id} was not found')
except BadRequest as e:
app.logger.error(e)
raise e
except NotFound as e:
app.logger.error(e)
raise e
except Exception as e:
app.logger.error(e)
abort(500, message=str(e))
def train(dataset, model_name, timestep=20):
"""Train an LSTM model."""
positions = []
for i in range(len(dataset[0])):
# model_period = f"{model_name}_period{i}.h5"
x_train, y_train = generate_time_series_sample(
normalize_data(dataset[0][i][0]), dataset[0][i][1].values,
timestep)
x_test, y_test = generate_time_series_sample(
normalize_data(dataset[1][i][0]), dataset[1][i][1].values,
timestep)
x_train = x_train.transpose((0, 2, 1))
x_train = np.reshape(x_train,
(x_train.shape[0] * x_train.shape[1], timestep))
y_train = np.reshape(y_train, (y_train.shape[0] * y_train.shape[1]))
x_test = x_test.transpose((0, 2, 1))
x_test = np.reshape(x_test,
(x_test.shape[0] * x_test.shape[1], timestep))
y_test = np.reshape(y_test, (y_test.shape[0] * y_test.shape[1]))
print(f"x train shape: {x_train.shape}")
print(f"y train shape: {y_train.shape}")
print(f"x test shape: {x_test.shape}")
print(f"y test shape: {y_test.shape}")
clf = RandomForestClassifier(n_jobs=2, random_state=0, max_depth=5)
clf.fit(x_train, y_train)
predict = clf.predict(x_test)
predict = predict.reshape(predict.shape[0] // 31, 31)[-250:]
position = dataset[1][i][1].values[-250:, :]
result = sum(sum(predict == position)) / predict.size
predict1 = clf.predict(x_test)
predict1 = predict1.reshape(predict1.shape[0] // 31, 31)[-300:-250]
position1 = dataset[1][i][1].values[-300:-250, :]
result1 = sum(sum(predict1 == position1)) / predict1.size
positions.append(predict)
print(result)
print(result1)
all_positions = np.concatenate(positions, axis=0)
print(all_positions.shape)
def pts_process(label_path, bbox, img_size):
landmark_ori = np.genfromtxt(label_path, skip_header=3, skip_footer=1)
landmark = np.multiply(
np.clip(
normalize_data(landmark_ori,
bbox,
occlu_include=False,
label_ext=".pts"), 0, 1), img_size)
return landmark
def return_test_data(self):
X_test = []
for i in range(self.test_images.shape[0]):
X = np.copy(self.test_images[i])
center = (int(np.floor(X.shape[1] / 2.)),
int(np.floor(X.shape[2] / 2.)))
X[:, center[0] - 16:center[0] + 16,
center[1] - 16:center[1] + 16] = 0
X_test.append(X)
y_test = []
for i in range(self.test_images.shape[0]):
y = np.copy(self.test_images[i])
center = (int(np.floor(y.shape[1] / 2.)),
int(np.floor(y.shape[2] / 2.)))
y_test.append(y[:, center[0] - 16:center[0] + 16,
center[1] - 16:center[1] + 16])
return normalize_data(np.array(X_test)), normalize_data(
np.array(y_test))
def train_deepnn(model_file, inputs, outputs, model, num_epochs):
x_train, x_valid, y_train, y_valid = train_test_split(inputs,
outputs,
test_size=0.2,
random_state=36)
means, std_dev = get_mean_stddev(x_train)
filepath = '/'.join(model_file.split("/")[:-1])
filename = model_file.split("_")[2] + "_" + str(x_train.shape[2])
np.save(filepath + "/means_" + filename + ".npy", means)
np.save(filepath + "/stddev_" + filename + ".npy", std_dev)
x_train = normalize_data(x_train, means, std_dev)
x_valid = normalize_data(x_valid, means, std_dev)
y_train = labels_to_categorical(y_train)
y_valid = labels_to_categorical(y_valid)
for epoch in range(num_epochs):
history_train = model.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
epochs=1,
verbose=0)
history_valid = model.evaluate(x_valid,
y_valid,
verbose=0,
batch_size=BATCH_SIZE)
key_list = list(history_train.history.keys())
score_train = history_train.history["loss"][0]
acc_train = history_train.history["acc"][0]
print()
print("Epoch {}/{}".format(epoch + 1, num_epochs))
print(" - loss: {:.4f} - acc: {:.4f}".format(score_train, acc_train))
print()
print("logloss score: %.4f" % history_valid[0])
print("Validation set Accuracy: %.4f" % history_valid[1])
add_history(model_file, history_train.history, history_valid, key_list)
return model
def get_data(opt, data, indices):
if not isinstance(indices, tuple):
indices = indices.split('_')
indices = (indices[0], int(indices[1]), int(indices[2]),
int(indices[3]))
x, flist = data.get_sequence_idx(*indices)
x = tobatch(x)
x = utils.normalize_data(opt, torch.cuda.FloatTensor, x)
name = '_'.join(list(map(str, indices)))
return x, name, flist
def get_batches_new(self, mode):
print('\n', '*' * 14)
print("creating data for {} mode".format(mode))
print('*' * 14)
in_features = self.data_config['in_features']
out_features = self.data_config['out_features']
data_obj = DATA(freq=self.data_config['freq'])
all_data = data_obj.get_df_from_rf('opt_set.mat') # INPUT
# copying required data
df = all_data[in_features].copy()
for out in out_features:
df[out] = all_data[out].copy()
if self.verbosity > 0:
print('shape of whole dataset', df.shape)
# assuming that pandas will add the 'datetime' column as last column. This columns will only be used to keep
# track of indices of train and test data.
df['datetime'] = list(
map(int, np.array(df.index.strftime('%Y%m%d%H%M'))))
index = all_data[mode + '_index']
ttk = index.dropna()
self.args[mode + '_args']['no_of_samples'] = len(ttk)
ttk_idx = list(map(int,
np.array(ttk.index.strftime('%Y%m%d%H%M')))) # list
df['to_keep'] = 0
df['to_keep'][ttk.index] = ttk_idx
dataset = nan_to_num(df.values,
len(out_features) + 2,
replace_with=0.0)
if self.data_config['normalize']:
dataset, self.scalers[mode] = normalize_data(
dataset, df.columns, 2)
self.batches[mode + '_x'],\
self.batches[mode + '_y'], \
self.nn_config[mode + '_no_of_batches'], \
self.batches[mode + '_index'],\
self.batches[mode + '_tk_index'] = generate_sample_based_batches(self.args[mode + '_args'],
self.nn_config['batch_size'],
dataset,
self.intervals[mode + '_intervals']
)
return
def __init__(self, config, train=True):
self.config = config
self.train = train
self.formatdata = FormatData(config)
if train:
subjects = os.listdir('{0}/{1}/{2}'.format(config.data_root,
'train',
config.filename))
else:
subjects = os.listdir('{0}/{1}/{2}'.format(config.data_root,
'test',
config.filename))
set = []
complete_train = []
for sub in subjects:
if train:
folderdir = '{0}/{1}/{2}/{3}'.format(config.data_root, 'train',
config.filename, sub)
else:
folderdir = '{0}/{1}/{2}/{3}'.format(config.data_root, 'test',
config.filename, sub)
for file in os.listdir(folderdir):
filedir = '{0}/{1}'.format(folderdir, file)
rawdata = np.load(filedir)['poses'][:, :66]
rawdata = self.frame_filter(rawdata)
# 去除帧太少的序列
if rawdata.shape[0] > 150:
set.append(rawdata)
if len(complete_train) == 0:
complete_train = copy.deepcopy(
set[-1]) #每个subjects取最后一个动作序列计算均值方差
else:
complete_train = np.append(complete_train, set[-1], axis=0)
if train:
print('video num for training：', len(set))
else:
print('video num for test：', len(set))
if not train and config.data_mean is None:
print('Load train dataset first!')
if train:
data_mean, data_std, dim_to_ignore, dim_to_use = utils.normalization_stats(
complete_train)
config.data_mean = data_mean
config.data_std = data_std
config.dim_to_ignore = dim_to_ignore
config.dim_to_use = dim_to_use
set = utils.normalize_data(set, config.data_mean, config.data_std,
config.dim_to_use)
# [S_num, frame_for_S, 60]
self.data = set
def test_out_digits(model, data, labels):
#print("===== TESTING THE CURRENT DIGITS =====")
rescaled = list(map(dutils.unpad_img, data))
rescaled = list(
map(
lambda img: dutils.center_box_image(dutils.resize_image(img, 20),
20, 4), rescaled))
testing_data = np.array(rescaled)
testing_data = utils.normalize_data(testing_data)
testing_data_size = testing_data.shape[0]
return ann.test_model(model,
testing_data.reshape(testing_data_size, 28, 28, 1),
labels)
def main():
# loading the datasets.
train_x = np.loadtxt("train_x")
train_y = np.loadtxt("train_y")
test_x = np.loadtxt("test_x")
train_x, train_y = utils.shuffle(train_x, train_y)
# data normalization
utils.normalize_data(train_x, 'min_max')
# Create Neaural network
net = nn.NeuralNetwork(train_x.shape)
# Training the model
net.train(train_x, train_y)
# Testing
result = net.test(test_x)
# Extract test result to file.
file = open('test_y', 'w+')
for y in result:
file.write("{}\n".format(y))
def tcf_cut(orig_datapoints, boundary_width=0.1, n=2):
"""
input: datapoints, table, type of method
output: two list of data in each cluster
"""
datapoints = deepcopy(orig_datapoints)
datapoints = utl.centralize_data(datapoints)
datapoints = utl.normalize_data(datapoints)
coeff, oa, ob = _tcf(datapoints)
c_left = []
c_right = []
r_bp = []
l_bp = []
r_nbp = []
l_nbp = []
for orig_point, copy_point in zip(orig_datapoints, datapoints):
# calc distance from point to boundary
unit_len = sum(coeff[:-1] ** 2) ** 0.5
p2b_dist = (sum(copy_point * coeff[:-1]) + coeff[-1]) / unit_len
if abs(p2b_dist) <= boundary_width * n:
if p2b_dist >= 0:
r_nbp.append(orig_point)
else:
l_nbp.append(orig_point)
if abs(p2b_dist) <= boundary_width:
if p2b_dist >= 0:
r_bp.append(orig_point)
else:
l_bp.append(orig_point)
if p2b_dist >= 0:
c_right.append(orig_point)
else:
c_left.append(orig_point)
c_left = np.array(c_left, np.float)
c_right = np.array(c_right, np.float)
r_bp = np.array(r_bp, np.float)
l_bp = np.array(l_bp, np.float)
r_nbp = np.array(r_nbp, np.float)
l_nbp = np.array(l_nbp, np.float)
# left, right, in boundary point, coeff
return c_left, c_right, (r_bp, l_bp), (r_nbp, l_nbp), coeff
def tcf_cut(orig_datapoints, boundary_width=0.1, n=2):
"""
input: datapoints, table, type of method
output: two list of data in each cluster
"""
datapoints = deepcopy(orig_datapoints)
datapoints = utl.centralize_data(datapoints)
datapoints = utl.normalize_data(datapoints)
coeff, oa, ob = _tcf(datapoints)
c_left = []
c_right = []
r_bp = []
l_bp = []
r_nbp = []
l_nbp = []
for orig_point, copy_point in zip(orig_datapoints, datapoints):
# calc distance from point to boundary
unit_len = sum(coeff[:-1]**2)**0.5
p2b_dist = (sum(copy_point * coeff[:-1]) + coeff[-1]) / unit_len
if abs(p2b_dist) <= boundary_width * n:
if p2b_dist >= 0:
r_nbp.append(orig_point)
else:
l_nbp.append(orig_point)
if abs(p2b_dist) <= boundary_width:
if p2b_dist >= 0:
r_bp.append(orig_point)
else:
l_bp.append(orig_point)
if p2b_dist >= 0:
c_right.append(orig_point)
else:
c_left.append(orig_point)
c_left = np.array(c_left, np.float)
c_right = np.array(c_right, np.float)
r_bp = np.array(r_bp, np.float)
l_bp = np.array(l_bp, np.float)
r_nbp = np.array(r_nbp, np.float)
l_nbp = np.array(l_nbp, np.float)
# left, right, in boundary point, coeff
return c_left, c_right, (r_bp, l_bp), (r_nbp, l_nbp), coeff
def rwm_cut(orig_datapoints, boundary_width=0.1, n=2):
datapoints = deepcopy(orig_datapoints)
datapoints = utl.centralize_data(datapoints)
datapoints = utl.normalize_data(datapoints)
in_boundary = 0
size, dim = datapoints.shape
c_left = []
c_right = []
coeff = _rwm(datapoints)
r_bp = []
l_bp = []
r_nbp = []
l_nbp = []
for orig_point, copy_point in zip(orig_datapoints, datapoints):
# calc distance from point to boundary
unit_len = sum(coeff[:-1] ** 2) ** 0.5
p2b_dist = (sum(copy_point * coeff[:-1]) + coeff[-1]) / unit_len
if abs(p2b_dist) <= boundary_width * n:
if p2b_dist >= 0:
r_nbp.append(orig_point)
else:
l_nbp.append(orig_point)
if abs(p2b_dist) <= boundary_width:
if p2b_dist >= 0:
r_bp.append(orig_point)
else:
l_bp.append(orig_point)
if p2b_dist >= 0:
c_right.append(orig_point)
else:
c_left.append(orig_point)
c_left = np.array(c_left, np.float)
c_right = np.array(c_right, np.float)
r_bp = np.array(r_bp, np.float)
l_bp = np.array(l_bp, np.float)
r_nbp = np.array(r_nbp, np.float)
l_nbp = np.array(l_nbp, np.float)
# left, right, in boundary point, coeff
return c_left, c_right, (r_bp, l_bp), (r_nbp, l_nbp), coeff
def experiment(network_model, reshape_mode = 'mlp'):
reshape_funs = {
"conv" : lambda d : d.reshape(-1,28,28,1),
"mlp" : lambda d : d.reshape(-1,784)
}
xtrain,ytrain,xtest,ytest = utils.load_mnist()
reshape_fun = reshape_funs[reshape_mode]
xtrain,xtest = reshape_fun(xtrain),reshape_fun(xtest)
digits_data = utils.load_processed_data('digits_og_and_optimal')
digits = digits_data['optimal_lw']
labels = utils.create_one_hot(digits_data['labels'].astype('uint'))
ensemble_size = 20
epochs = 50
small_digits = reshape_fun(np.array(list(map(scale_down, digits))))
small_digits = utils.normalize_data(small_digits)
trials = 5
for t in range(1,trials+1):
gc.collect()
l_xtrain = []
l_xval = []
l_ytrain = []
l_yval = []
for _ in range(ensemble_size):
t_xtrain,t_ytrain,t_xval,t_yval = utils.create_validation(xtrain,ytrain,(1/6))
l_xtrain.append(t_xtrain)
l_xval.append(t_xval)
l_ytrain.append(t_ytrain)
l_yval.append(t_yval)
inputs, outputs, train_model, model_list, merge_model = ann.build_ensemble([network_model], pop_per_type=ensemble_size, merge_type="Average")
es = clb.EarlyStopping(monitor='val_loss',patience=2,restore_best_weights=True)
train_model.compile(optimizer="adam", loss="categorical_crossentropy", metrics = ['acc'])
train_model.fit(x=l_xtrain,y=l_ytrain, verbose=1,batch_size=100, epochs = epochs,validation_data=(l_xval,l_yval),callbacks=[es])
merge_model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=['acc'])
results = test_digits(merge_model, digits, labels, ensemble_size, reshape_fun)
#entropy = ann.test_model(merge_model, [small_digits]*ensemble_size, labels, metric = 'entropy')
#c_error = ann.test_model(merge_model, [small_digits]*ensemble_size, labels, metric = 'c_error')
#results['c_error'][0] = c_error
#results['entropy'][0] = entropy
filename = "saltpepper_norm_trial-%s" % t
utils.save_processed_data(results, filename)
def __init__(self, config, train=True):
self.config = config
self.train = train
self.formatdata = FormatData(config)
if config.datatype == 'smpl':
train_path = config.data_root
else:
print('CMUDataset only support the smpl datatype')
sys.exit(1)
if config.filename != 'all':
if train:
subjects = config.subjects_train
else:
subjects = config.subjects_test
else:
print('Only support walking and dance action')
sys.exit(1)
set = []
complete_train = []
for sub in subjects:
folderdir = '{0}/{1}'.format(train_path, sub)
for file in os.listdir(folderdir):
filedir = '{0}/{1}'.format(folderdir, file)
rawdata = np.load(filedir)['poses'][:, :66]
rawdata = self.frame_filter(rawdata)
if rawdata.shape[0] > 70:
set.append(rawdata)
if len(complete_train) == 0:
complete_train = copy.deepcopy(
set[-1]) #每个subjects取最后一个动作序列计算均值方差
else:
complete_train = np.append(complete_train, set[-1], axis=0)
print('视频个数：', len(set))
if not train and config.data_mean is None:
print('Load train dataset first!')
if train and config.datatype == 'smpl':
data_mean, data_std, dim_to_ignore, dim_to_use = utils.normalization_stats(
complete_train)
config.data_mean = data_mean
config.data_std = data_std
config.dim_to_ignore = dim_to_ignore
config.dim_to_use = dim_to_use
set = utils.normalize_data(set, config.data_mean, config.data_std,
config.dim_to_use)
# [S_num, frame_for_S, 66]
self.data = set
def single_file_reader(savename):
f = open(savename)
full_x = []
full_y = []
for line in f:
(x,y) = line.split(' ')
x = map(float, x.split(',')[:-1])
y = map(int, y.split(',')[:-1])
full_x.append(x)
full_y.append(y)
full_x = utils.normalize_data(full_x)
return (full_x, full_y)
def preprocess_and_save(batch_id):
images, labels = load_cifar10_batch(batch_id)
images = utils.normalize_data(images)
labels = utils.one_hot_encode(labels, 10)
train_images, train_labels, valid_images, valid_labels, test_images, test_labels =\
utils.split_data(images, labels, train_size=0.8, valid_size=0.1, test_size=0.1)
batch = {
'train_images': train_images,
'train_labels': train_labels,
'valid_images': valid_images,
'valid_labels': valid_labels,
'test_images': test_images,
'test_labels': test_labels
}
batch_path = os.path.join(folder_path, 'preprocess_batch_' + str(batch_id))
np.save(batch_path, np.asarray(batch))
def select_features_stepwise_forward(dataFrame, n_news, original_cols):
"""
*stepwise selection* de varaibles, utiliza las importancias de un modelo de random forest para clasificar las mejores variables
Parámetros:
- dataFrame -- *DataFrame* de pandas, datos de todas las variables que van a ser seleccionadas
- n_news -- Entero, máximo número de variables que van a ser seleccionadas
- original_cols -- Lista, lista con los nombres de las columnas originales del problema para reconocer las variables seleccionadas
Retorna:
NADA
(no retorna nada pero escribe las variables seleccioandas en el archivo 'data_selected.csv' en el directorio data)
"""
n_features = dataFrame.shape[1]
dataFrame.columns = original_cols
# params
n_news -= 1
features = set(dataFrame.columns)
features.remove(list(dataFrame.columns)[0])
missing = features.copy()
inside = [list(dataFrame.columns)[0]]
from sklearn.ensemble import RandomForestRegressor
while (n_news):
fts = list(inside)
best = ''
best_importance = 0
for ft in missing:
fts = fts + [ft]
scaled, scaler = utils.normalize_data(dataFrame[fts].values)
x, y = series_to_supervised(scaled)
model = RandomForestRegressor(n_estimators=100)
model.fit(x, y)
importances = model.feature_importances_
if (importances[-1] > best_importance):
best = fts[-1]
best_importance = importances[-1]
inside.append(best)
missing.remove(best)
n_news -= 1
df = dataFrame[inside]
df.to_csv('data/data_selected.csv')
def cut_by_coeff(orig_datapoints, coeff):
datapoints = deepcopy(orig_datapoints)
datapoints = utl.centralize_data(datapoints)
datapoints = utl.normalize_data(datapoints)
c_left = []
c_right = []
unit_len = sum(coeff[:-1] ** 2) ** 0.5
for orig_point, copy_point in zip(orig_datapoints, datapoints):
p2b_dist = (sum(copy_point * coeff[:-1]) + coeff[-1]) / unit_len
if p2b_dist >= 0:
c_right.append(orig_point)
else:
c_left.append(orig_point)
c_left = np.array(c_left, np.float)
c_right = np.array(c_right, np.float)
return (c_left, c_right)
def cut_by_coeff(orig_datapoints, coeff):
datapoints = deepcopy(orig_datapoints)
datapoints = utl.centralize_data(datapoints)
datapoints = utl.normalize_data(datapoints)
c_left = []
c_right = []
unit_len = sum(coeff[:-1]**2)**0.5
for orig_point, copy_point in zip(orig_datapoints, datapoints):
p2b_dist = (sum(copy_point * coeff[:-1]) + coeff[-1]) / unit_len
if p2b_dist >= 0:
c_right.append(orig_point)
else:
c_left.append(orig_point)
c_left = np.array(c_left, np.float)
c_right = np.array(c_right, np.float)
return (c_left, c_right)
def plot_hilbert_spectra(time, frequency, amplitude, title, plotter=plt, fs=100):
# Scale factor (to plot frequency with decimal precision)
scale_freq = 10
# Max scaled frequency
max_freq = int(0.5*scale_freq*fs)
# Creating time axis
time_ax = np.linspace(0, len(time)-1, len(time))
# Allocating memory for power and the rounded frequency
power_array = np.zeros(np.shape(frequency))
freq_rounded_array = np.zeros(np.shape(power_array), np.int)
# Create GRID based on time axis and maximum frequency
yi = np.linspace(0, max_freq, max_freq + 1)
Z = np.ones((max_freq + 1, len(time_ax)))*-200
X, Y = np.meshgrid(time_ax, yi)
# Enter loop if more than one IMF exists
if isinstance(frequency[0], np.ndarray):
n_inst_frequencies = len(frequency)
for i in range(n_inst_frequencies):
# Normalize the amplitude ( 0<=a<=1)
amplitude[i] = utils.normalize_data(amplitude[i])
# Power equal to amplitude squared
power_array[i] = np.multiply(amplitude[i], amplitude[i])
# Round the frequency to the nearest (results in OK resolution if scale_freq > 1, eg scale_freq=10)
freq_rounded_array[i] = np.ceil(frequency[i]*scale_freq)
# Compute the logarithmic power, and add it to the previous if the same inst. frequency exists.
for k in range(len(time_ax)):
if power_array[i, k] == 0.0:
power_array[i, k] = 0.00000001
current_amplitude = Z[int(freq_rounded_array[i, k]), int(time_ax[k])]
if current_amplitude > -200:
Z[int(freq_rounded_array[i, k]), int(time_ax[k])] = current_amplitude + 20.0*np.log10(power_array[i, k])
else:
Z[int(freq_rounded_array[i, k]), int(time_ax[k])] = 20.0*np.log10(power_array[i, k])
else:
# Normalize the amplitude ( 0<=a<=1)
amplitude = utils.normalize_data(amplitude)
# Power equal to amplitude squared
power_array = np.multiply(amplitude, amplitude)
# Round the frequency to the nearest (results in OK resolution if scale_freq > 1, eg scale_freq=10)
freq_rounded_array = np.ceil(frequency*scale_freq)
# Compute the logarithmic power, and add it to the previous if the same inst. frequency exists.
for k in range(len(time_ax)):
Z[int(freq_rounded_array[k]), int(time_ax[k])] = 20.0*np.log10(power_array[k])
# Create figure and subplot.
# Set titles and labels.
fig = plotter.figure()
suptitle = 'Hilbert Spectra - Channel: ' + title
fig.suptitle(suptitle)
ax = plotter.subplot(111)
ax.set_xlabel('Time [s]')
ax.set_ylabel('Frequency [Hz]')
# Create contour plot and time, frequency and logarithmic power. Scale frequencies back to original values.
n_levels = 200
cax = ax.contourf(X, Y/scale_freq, Z, n_levels)
# Assign color bar to the contour plot
cb = fig.colorbar(cax)
# Set label and draw plot
cb.set_label('Amplitude [dB]')
plotter.draw()
for cls in tmp_clusters:
res_cls.append(cls)
print("#cls {} -> {}".format(len(clusters), len(res_cls)))
print(calc_num_point(res_cls))
return res_cls
if __name__ == '__main__':
doctest.testmod()
points, label = utl.read_from_text('2d5c_noncycle')
points = utl.centralize_data(points)
points = utl.normalize_data(points)
# points, label = utl.read_from_text('2d5c_cov')
# points, label = utl.read_from_text('hand_write_digit_2d')
# seleted = datasets.load_digits()
# points = seleted.data
# label = seleted.target
#
ms_tree = ms2c(points)
# paint_tree(ms_tree, ms_tree)
final_nodes = ms_tree.merge()
grounded_nodes = ms_tree.grounded_nodes
grounded_cls = [x.datapoints for x in grounded_nodes]
final_cls = [x.datapoints for x in final_nodes]
# Predicting house prices: a regression example
from keras.datasets import boston_housing
from utils import normalize_data
from keras import models, layers, optimizers, losses, metrics
# loading the Boston housing dataset
(train_data, train_labels), (test_data, test_labels) = boston_housing.load_data()
# dataset info
print(train_data.shape)
print(test_data.shape)
# preparing the data
# Normalizing the data
train_data = normalize_data(train_data)
test_data = normalize_data(test_data)
# build your network
def build_model():
model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(train_data.shape[1],)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(1))
model.compile(optimizer=optimizers.Adam(lr=0.001), loss=losses.mse, metrics=[metrics.mae])
return model
network = build_model()
# train
network.fit(train_data, train_labels, epochs=80)
loss, acc = network.evaluate(test_data, test_labels)
print(loss, acc)
import numpy as np
from keras import models
from keras import layers
from keras import optimizers
from keras import losses
from keras import metrics
from keras.datasets import boston_housing
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from utils import normalize_data, init_keras
init_keras()
(x_train, y_train),(x_test, y_test) = boston_housing.load_data()
normalize_data(x_train)
normalize_data(x_test)
k = 4
epochs = 500
mae_histories_4_k_fold = []
for i in range(k):
x_train, x_cv, y_train, y_cv = train_test_split(
x_train, y_train, test_size=1.0/k)
model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(x_train.shape[1],)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(1))
model.compile(optimizer=optimizers.RMSprop(lr=0.001), loss=losses.mse, metrics=[metrics.mae])
def get_testing_batch(dtype=torch.cuda.FloatTensor):
while True:
for sequence in test_loader:
batch = utils.normalize_data(opt, dtype, sequence)
yield batch
def read_data(trainfile, batchsize=100000, platenamefile=None):
"""
Reads a datafile created by the generate_data_files function.
It will reset the file after having read through it.
Yields data in batches with a specified batchsize.
Parameters:
trainfile: The file to read data from.
The must have a seek function taking an integer as the parameter.
batchsize: How many samples to yield at a time.
Defaults to 100000
platenamefile:
A file to read platenammes from, which should correspond to the rows in the trainfile.
If None, no platenames will be read or yielded.
Defaults to None.
Returns:
Data on the format (features,target) or (features,target,platenames) if platenamefile is set,
in batches of size `batchsize`.
"""
while True:
x = trainfile.readline()
y = trainfile.readline()
if y == '':
break
if platenamefile:
platestring = platenamefile.readline()
platestring = platestring.split(' ')
platestring[1] = int(platestring[1])
platestring[2] = int(platestring[2])
platenames = [platestring]
try:
x = [map(float, x.split(' ')[:-1])]
y = [map(int, y.split(' ')[:-1])]
except:
x = []
y = []
if platenamefile:
del(platenames[-1])
while len(x) < batchsize:
newx = trainfile.readline()
newy = trainfile.readline()
if newy == '':
break
if platenamefile:
platestring = platenamefile.readline()
platestring = platestring.split(' ')
platestring[1] = int(platestring[1])
platestring[2] = int(platestring[2])
platenames.append(platestring)
#Remove newlines and convert to correct datatypes
try:
x.append(map(float, newx.split(' ')[:-1]))
y.append(map(int, newy.split(' ')[:-1]))
except:
if platenamefile:
del(platenames[-1])
continue
x = utils.normalize_data(x)
if platenamefile:
yield(x,y,platenames)
else:
yield(x,y)
trainfile.seek(0)
if platenamefile:
platenamefile.seek(0)
def get_testing_batch():
while True:
for sequence in test_loader:
batch = utils.normalize_data(opt, dtype, sequence)
yield batch
