def get_encoder(shape, C=1, name="encoder"):
inputs = Input(shape=(shape, ))
modeled = Dense(100,
activation='relu',
kernel_constraint=MinMaxNorm(0, C),
bias_constraint=MinMaxNorm(0, C))(inputs)
model = Model(inputs, modeled)
model.compile(optimizer="adam", loss='mean_squared_error')
return model
def get_task(shape, C=1, activation=None, name="task"):
inputs = Input(shape=(shape, ))
modeled = Dense(1,
activation=activation,
kernel_constraint=MinMaxNorm(0, C),
bias_constraint=MinMaxNorm(0, C))(inputs)
model = Model(inputs, modeled)
model.compile(optimizer="adam", loss='mean_squared_error')
return model
def get_base_model(shape, activation=None, C=1, name="BaseModel"):
inputs = Input(shape=(shape, ))
modeled = Dense(100,
activation='relu',
kernel_constraint=MinMaxNorm(0, C),
bias_constraint=MinMaxNorm(0, C))(inputs)
modeled = Dense(1,
activation=activation,
kernel_constraint=MinMaxNorm(0, C),
bias_constraint=MinMaxNorm(0, C))(modeled)
model = Model(inputs, modeled, name=name)
model.compile(optimizer='adam', loss='mean_squared_error')
return model
def build(self, input_shape):
if self.type_code == 'Binary_0':
H_init = np.random.rand(1, self.kern, self.kern, self.shots)
#H_init = np.random.normal(0, 1, (1, self.kern, self.kern, 1, self.shots)) / np.sqrt(
#self.kern * self.kern)+0.5
H_init = tf.constant_initializer(H_init)
self.H = self.add_weight(name='H', shape=(1,self.kern, self.kern, 1,self.shots), initializer=H_init, trainable=True,
regularizer=self.my_regularizer)
# --------Binary -1 1 -------
if self.type_code == 'Binary_1':
H_init = np.random.normal(0, 1, (1, self.kern, self.kern,1,self.shots)) / np.sqrt(
self.kern * self.kern)
H_init = tf.constant_initializer(H_init)
self.H = self.add_weight(name='H', shape=(1, self.kern, self.kern,1,self.shots),
initializer=H_init, trainable=True,
regularizer=self.my_regularizer)
# --------Gray scale -------
if self.type_code == 'Gray_scale':
H_init = np.random.normal(0, 1, (1, self.kern, self.kern, self.shots)) / np.sqrt(
self.kern * self.kern)
H_init = tf.constant_initializer(H_init)
if self.type_reg == 'Physical':
self.H = self.add_weight(name='H', shape=(1, self.kern, self.kern,1,self.shots),
initializer=H_init, trainable=True,
constraint=MinMaxNorm(min_value=0.0, max_value=1.0, rate=1.0))
def create_model(input_shape, num_classes):
""" Create a model to train on MNIST data. """
# ensure weights and bias values are clipped
clip = MinMaxNorm(min_value=-0.5, max_value=0.5, axis=0)
inp = Input(shape=input_shape)
x = Reshape((28, 28, 1))(inp)
x = Conv2D(32,
kernel_size=(3, 3),
activation="relu",
kernel_constraint=clip,
bias_constraint=clip)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Conv2D(64,
kernel_size=(3, 3),
activation="relu",
kernel_constraint=clip,
bias_constraint=clip)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Flatten()(x)
x = Dropout(0.4)(x)
x = Dense(num_classes,
activation='softmax',
kernel_constraint=clip,
bias_constraint=clip)(x)
model = Model(inputs=inp, outputs=x)
return model
def __init__(self, num_units, input_size, output_size):
super(MLP, self).__init__()
self.layer1 = layers.Dense(num_units,
activation='relu',
input_shape=(input_size, ),
kernel_regularizer=L2(l2=0.01),
kernel_constraint=MinMaxNorm(min_value=-5.0,
max_value=5.0))
self.layer2 = layers.Dense(num_units,
activation='relu',
input_shape=(num_units, ),
kernel_regularizer=L2(l2=0.01),
kernel_constraint=MinMaxNorm(min_value=-5.0,
max_value=5.0))
self.layer3 = layers.Dense(output_size,
input_shape=(num_units, ),
kernel_regularizer=L2(l2=0.01))
def PReLUlip(k_coef_lip=1.0):
"""
PreLu activation, with Lipschitz constraint.
Args:
k_coef_lip: lipschitz coefficient to be enforced
"""
return PReLU(alpha_constraint=MinMaxNorm(min_value=-k_coef_lip,
max_value=k_coef_lip))
def build(self, input_shape, **kwargs):
self.threshold1 = self.add_weight("threshold1",
shape=[1],
initializer=self.kernel_initializer,
constraint=MinMaxNorm(min_value=0.0,
max_value=0.3,
rate=1.0),
dtype=self.dtype,
trainable=self.trainable,
**kwargs)
self.threshold2 = self.add_weight("threshold2",
shape=[1],
initializer=self.kernel_initializer,
constraint=MinMaxNorm(min_value=0.2,
max_value=0.5,
rate=1.0),
dtype=self.dtype,
trainable=self.trainable,
**kwargs)
self.threshold3 = self.add_weight("threshold3",
shape=[1],
initializer=self.kernel_initializer,
constraint=MinMaxNorm(min_value=0.4,
max_value=0.8,
rate=1.0),
dtype=self.dtype,
trainable=self.trainable,
**kwargs)
self.threshold4 = self.add_weight("threshold4",
shape=[1],
initializer=self.kernel_initializer,
constraint=MinMaxNorm(min_value=0.8,
max_value=2.0,
rate=1.0),
dtype=self.dtype,
trainable=self.trainable,
**kwargs)
self.built = True
def build(self, shape):
weight_constraint = MinMaxNorm(min_value=0.0,
max_value=1.0,
rate=1.0,
axis=0)
self.w1 = self.add_weight(name='w1',
shape=(shape[3], ),
initializer="ones",
trainable=True,
constraint=weight_constraint)
# self.w2 = self.add_weight(name='w2', shape=(1,), initializer="ones", trainable=True)
self.w2 = 1 - self.w1
def build(self, input_shape):
M = round((self.input_dim[0] * self.input_dim[1] * self.input_dim[2]) *
self.compression)
# --------------------------- Types of codes initial values ---------------------------------------
# --------Binary -1 1 -------
if self.type_code == 'Binary_1':
H_init = np.random.normal(0, 1,
(1, self.kern, self.kern, M)) / np.sqrt(
self.kern * self.kern)
H_init = tf.constant_initializer(H_init)
self.H = self.add_weight(name='H',
shape=(1, self.kern, self.kern, M),
initializer=H_init,
trainable=True,
regularizer=self.my_regularizer)
# --------Binary 0,1 -------
if self.type_code == 'Binary_0':
#H_init = np.random.rand(1, self.kern, self.kern, M)
H_init = np.random.normal(0, 1,
(1, self.kern, self.kern, M)) / np.sqrt(
self.kern * self.kern)
H_init = tf.constant_initializer(H_init)
self.H = self.add_weight(name='H',
shape=(1, self.kern, self.kern, M),
initializer=H_init,
trainable=True,
regularizer=self.my_regularizer)
# --------Gray scale -------
if self.type_code == 'Gray_scale':
H_init = np.random.normal(0, 1,
(1, self.kern, self.kern, M)) / np.sqrt(
self.kern * self.kern)
H_init = tf.constant_initializer(H_init)
if self.type_reg == 'Physical':
self.H = self.add_weight(name='H',
shape=(1, self.kern, self.kern, M),
initializer=H_init,
trainable=True,
constraint=MinMaxNorm(min_value=0.0,
max_value=1.0,
rate=1.0))
super(Single_Pixel_Layer_trunc, self).build(input_shape)
def createBaseline(learning_rate, l2_factor, dropout_factor):
# Create CNN from the paper.
model = Sequential()
# Starts with input 2D convolutional layer with input shape matching the images shaper and
# l2 regularization enabled.
model.add(Conv2D(96, (11, 11), input_shape=(512, 512, 1), kernel_regularizer=l2(l2_factor), bias_regularizer=l2(l2_factor)))
# Use batch normalization for the first layer, then do activation and pooling.
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D((3, 3)))
# Add next 2D convolutional layer with smaller filter size and the same regularization as
# in the first layer.
model.add(Conv2D(384, (5, 5), kernel_regularizer=l2(l2_factor), bias_regularizer=l2(l2_factor)))
model.add(Activation('relu'))
model.add(MaxPooling2D((3, 3)))
# The next three layers are almost the same, having similar approach to VGG19 with multiple
# convolutional layers connected in sequence before pooling and all having 3x3 filters
# (a.k.a. kernels). The filter count decreases slightly to account for compression of
# information the farther it goes through the network. All layers use l2 regularization.
model.add(Conv2D(384, (3, 3), activation='relu', kernel_regularizer=l2(l2_factor), bias_regularizer=l2(l2_factor)))
model.add(Conv2D(256, (3, 3), activation='relu', kernel_regularizer=l2(l2_factor), bias_regularizer=l2(l2_factor)))
model.add(Conv2D(256, (3, 3), kernel_regularizer=l2(l2_factor), bias_regularizer=l2(l2_factor)))
# Apply activation and pooling.
model.add(Activation('relu'))
model.add(MaxPooling2D((3, 3)))
# Flatten the outputs form 2 dimensional layers to be ready for fully-connected layers.
model.add(Flatten())
# Create fully connected layers with dropout layers in-between.
model.add(Dense(4096, activation='relu'))
model.add(Dropout(dropout_factor))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(dropout_factor))
model.add(Dense(1, activation='sigmoid', bias_constraint=MinMaxNorm(min_value=0.1, max_value=0.9)))
# Compile the model with Adam optimizer and Binary Crossentropy loss function for binary
# classification. The metrics used for this model are accuracy, mean-square-error,
# precision and recall.
model.compile(optimizer=Adam(learning_rate=learning_rate),
loss=BinaryCrossentropy(from_logits=True),
metrics=['accuracy', 'mse', Precision(), Recall()])
return model
def __init__(self, output_dim, dicparams, **kwargs):
const = MinMaxNorm(
min_value=-dicparams.get("MinNorm", 1),
max_value=dicparams.get("MaxNorm", 1e-5),
rate=dicparams.get("NormRate", 1.0),
axis=0
)
bias_init = dicparams.get("bias_initializer", "zeros")
self.units = output_dim
self.kconstraint = constraints.get(const)
self.kinitializer1 = Identity()
self.kinitializer2 = initializers.get(GenKinit())
self.binitializer = get_init(bias_init)
self.use_bias = dicparams.get("use_bias", False)
self.activation = dicparams.get("activation")
super(GenDense, self).__init__(**kwargs)
def get_base_model(input_shape=(166, ), output_shape=(1, ), C=1):
inputs = Input(shape=input_shape)
modeled = Flatten()(inputs)
modeled = Dense(100,
activation='relu',
kernel_constraint=MinMaxNorm(0, C),
bias_constraint=MinMaxNorm(0, C))(modeled)
modeled = Dense(100,
activation='relu',
kernel_constraint=MinMaxNorm(0, C),
bias_constraint=MinMaxNorm(0, C))(modeled)
modeled = Dense(np.prod(output_shape),
activation=None,
kernel_constraint=MinMaxNorm(0, C),
bias_constraint=MinMaxNorm(0, C))(modeled)
model = Model(inputs, modeled)
model.compile(optimizer=Adam(0.001), loss='mean_squared_error')
return model
def get_base_model(
input_shape=(768, ), output_shape=10, activation="softmax", C=1):
inputs = Input(input_shape)
modeled = Dense(100,
activation='relu',
kernel_constraint=MinMaxNorm(0, C),
bias_constraint=MinMaxNorm(0, C))(inputs)
modeled = Dense(100,
activation='relu',
kernel_constraint=MinMaxNorm(0, C),
bias_constraint=MinMaxNorm(0, C))(modeled)
modeled = Dense(10,
activation=activation,
kernel_constraint=MinMaxNorm(0, C),
bias_constraint=MinMaxNorm(0, C))(modeled)
model = Model(inputs, modeled)
model.compile(optimizer=Adam(0.001),
loss='categorical_crossentropy',
metrics=["accuracy"])
return model
def compile_elmo(self, print_summary=False):
"""
Compiles a Language Model RNN based on the given parameters
"""
if self.parameters['token_encoding'] == 'word':
# Train word embeddings from scratch
word_inputs = Input(shape=(None, ),
name='word_indices',
dtype='int32')
embeddings = Embedding(self.parameters['vocab_size'],
self.parameters['hidden_units_size'],
trainable=True,
name='token_encoding')
inputs = embeddings(word_inputs)
# Token embeddings for Input
drop_inputs = SpatialDropout1D(
self.parameters['dropout_rate'])(inputs)
lstm_inputs = TimestepDropout(
self.parameters['word_dropout_rate'])(drop_inputs)
# Pass outputs as inputs to apply sampled softmax
next_ids = Input(shape=(None, 1), name='next_ids', dtype='float32')
previous_ids = Input(shape=(None, 1),
name='previous_ids',
dtype='float32')
elif self.parameters['token_encoding'] == 'char':
# Train character-level representation
word_inputs = Input(shape=(
None,
self.parameters['token_maxlen'],
),
dtype='int32',
name='char_indices')
inputs = self.char_level_token_encoder()(word_inputs)
# Token embeddings for Input
drop_inputs = SpatialDropout1D(
self.parameters['dropout_rate'])(inputs)
lstm_inputs = TimestepDropout(
self.parameters['word_dropout_rate'])(drop_inputs)
# Pass outputs as inputs to apply sampled softmax
next_ids = Input(shape=(None, 1), name='next_ids', dtype='float32')
previous_ids = Input(shape=(None, 1),
name='previous_ids',
dtype='float32')
# Reversed input for backward LSTMs
re_lstm_inputs = Lambda(function=ELMo.reverse)(lstm_inputs)
mask = Lambda(function=ELMo.reverse)(drop_inputs)
# Forward LSTMs
for i in range(self.parameters['n_lstm_layers']):
if self.parameters['cuDNN']:
lstm = LSTM(units=self.parameters['lstm_units_size'],
return_sequences=True,
kernel_constraint=MinMaxNorm(
-1 * self.parameters['cell_clip'],
self.parameters['cell_clip']),
recurrent_constraint=MinMaxNorm(
-1 * self.parameters['cell_clip'],
self.parameters['cell_clip']))(lstm_inputs)
else:
lstm = LSTM(units=self.parameters['lstm_units_size'],
return_sequences=True,
activation="tanh",
recurrent_activation='sigmoid',
kernel_constraint=MinMaxNorm(
-1 * self.parameters['cell_clip'],
self.parameters['cell_clip']),
recurrent_constraint=MinMaxNorm(
-1 * self.parameters['cell_clip'],
self.parameters['cell_clip']))(lstm_inputs)
lstm = Camouflage(mask_value=0)(inputs=[lstm, drop_inputs])
# Projection to hidden_units_size
proj = TimeDistributed(
Dense(self.parameters['hidden_units_size'],
activation='linear',
kernel_constraint=MinMaxNorm(
-1 * self.parameters['proj_clip'],
self.parameters['proj_clip'])))(lstm)
# Merge Bi-LSTMs feature vectors with the previous ones
lstm_inputs = add([proj, lstm_inputs],
name='f_block_{}'.format(i + 1))
# Apply variational drop-out between BI-LSTM layers
lstm_inputs = SpatialDropout1D(
self.parameters['dropout_rate'])(lstm_inputs)
# Backward LSTMs
for i in range(self.parameters['n_lstm_layers']):
if self.parameters['cuDNN']:
re_lstm = LSTM(
units=self.parameters['lstm_units_size'],
return_sequences=True,
kernel_constraint=MinMaxNorm(
-1 * self.parameters['cell_clip'],
self.parameters['cell_clip']),
recurrent_constraint=MinMaxNorm(
-1 * self.parameters['cell_clip'],
self.parameters['cell_clip']))(re_lstm_inputs)
else:
re_lstm = LSTM(
units=self.parameters['lstm_units_size'],
return_sequences=True,
activation='tanh',
recurrent_activation='sigmoid',
kernel_constraint=MinMaxNorm(
-1 * self.parameters['cell_clip'],
self.parameters['cell_clip']),
recurrent_constraint=MinMaxNorm(
-1 * self.parameters['cell_clip'],
self.parameters['cell_clip']))(re_lstm_inputs)
re_lstm = Camouflage(mask_value=0)(inputs=[re_lstm, mask])
# Projection to hidden_units_size
re_proj = TimeDistributed(
Dense(self.parameters['hidden_units_size'],
activation='linear',
kernel_constraint=MinMaxNorm(
-1 * self.parameters['proj_clip'],
self.parameters['proj_clip'])))(re_lstm)
# Merge Bi-LSTMs feature vectors with the previous ones
re_lstm_inputs = add([re_proj, re_lstm_inputs],
name='b_block_{}'.format(i + 1))
# Apply variational drop-out between BI-LSTM layers
re_lstm_inputs = SpatialDropout1D(
self.parameters['dropout_rate'])(re_lstm_inputs)
# Reverse backward LSTMs' outputs = Make it forward again
re_lstm_inputs = Lambda(function=ELMo.reverse,
name="reverse")(re_lstm_inputs)
# Project to Vocabulary with Sampled Softmax
sampled_softmax = SampledSoftmax(
num_classes=self.parameters['vocab_size'],
num_sampled=int(self.parameters['num_sampled']),
tied_to=embeddings if self.parameters['weight_tying']
and self.parameters['token_encoding'] == 'word' else None)
outputs = sampled_softmax([lstm_inputs, next_ids])
re_outputs = sampled_softmax([re_lstm_inputs, previous_ids])
self._model = Model(inputs=[word_inputs, next_ids, previous_ids],
outputs=[outputs, re_outputs])
self._model.compile(optimizer=tf.keras.optimizers.Adagrad(
lr=self.parameters['lr'], clipvalue=self.parameters['clip_value']),
loss=None)
if print_summary:
self._model.summary()
def create_calibration_model(
storage_coefs, loss_coefs, radius, length, thickness, poisson, inertia,
storage_data, storage_bounds, storage_table_shape, loss_data,
loss_bounds, loss_table_shape, delta_stiffness_mlp, delta_damping_mlp,
stiffness_low, stiffness_up, damping_low, damping_up, input_min,
input_range, select_freq, select_temp, batch_input_shape, myDtype):
batch_adjusted_shape = (batch_input_shape[1], )
inputLayer = Input(shape=(batch_input_shape[1], ))
normalizedInputLayer = Lambda(
lambda x, input_min=input_min, input_range=input_range:
(x - input_min) / input_range)(inputLayer)
freqLayer = inputsSelection(batch_adjusted_shape, select_freq)(inputLayer)
tempLayer = inputsSelection(batch_adjusted_shape, select_temp)(inputLayer)
omegaLayer = Lambda(lambda x: 2 * np.pi * x)(freqLayer)
moduliInputLayer = Concatenate(axis=-1)([tempLayer, freqLayer])
storageModulusLayer = TableInterpolation(table_shape=storage_table_shape,
dtype=myDtype,
trainable=False)
storageModulusLayer.build(input_shape=moduliInputLayer.shape)
storageModulusLayer.set_weights([storage_data, storage_bounds])
storageModulusLayer = storageModulusLayer(moduliInputLayer)
lossModulusLayer = TableInterpolation(table_shape=loss_table_shape,
dtype=myDtype,
trainable=False)
lossModulusLayer.build(input_shape=moduliInputLayer.shape)
lossModulusLayer.set_weights([loss_data, loss_bounds])
lossModulusLayer = lossModulusLayer(moduliInputLayer)
stiffnessLayer = Stiffness(input_shape=storageModulusLayer.shape,
dtype=myDtype,
trainable=True,
kernel_constraint=MinMaxNorm(min_value=-1.0,
max_value=0.5,
rate=1.0))
stiffnessLayer.build(input_shape=storageModulusLayer.shape)
stiffnessLayer.set_weights(
[np.asarray([poisson], dtype=stiffnessLayer.dtype)])
stiffnessLayer = stiffnessLayer(storageModulusLayer)
deltaStiffnessLayer = delta_stiffness_mlp(normalizedInputLayer)
scaledDeltaStiffnessLayer = Lambda(
lambda x, stiffness_low=stiffness_low, stiffness_up=stiffness_up: x *
(stiffness_up - stiffness_low) + stiffness_low)(deltaStiffnessLayer)
correctedStiffnessLayer = Lambda(lambda x: x[0] + x[1])(
[stiffnessLayer, scaledDeltaStiffnessLayer])
dampingInputLayer = Concatenate(axis=-1)(
[correctedStiffnessLayer, storageModulusLayer, lossModulusLayer])
dampingLayer = Damping(input_shape=dampingInputLayer.shape,
dtype=myDtype,
trainable=False)
dampingLayer.build(input_shape=dampingInputLayer.shape)
dampingLayer.set_weights([np.asarray([inertia], dtype=dampingLayer.dtype)])
dampingLayer = dampingLayer(dampingInputLayer)
deltaDampingLayer = delta_damping_mlp(normalizedInputLayer)
scaledDeltaDampingLayer = Lambda(
lambda x, damping_low=damping_low, damping_up=damping_up: x *
(damping_up - damping_low) + damping_low)(deltaDampingLayer)
correctedDampingLayer = Lambda(lambda x: x[0] + x[1])(
[dampingLayer, scaledDeltaDampingLayer])
FRFAmpInputLayer = Concatenate(axis=-1)(
[omegaLayer, correctedStiffnessLayer, correctedDampingLayer])
FRFAmpLayer = FRFAmplitude(input_shape=FRFAmpInputLayer.shape,
dtype=myDtype,
trainable=False)
FRFAmpLayer.build(input_shape=FRFAmpInputLayer.shape)
FRFAmpLayer.set_weights([np.asarray([inertia], dtype=FRFAmpLayer.dtype)])
FRFAmpLayer = FRFAmpLayer(FRFAmpInputLayer)
functionalModel = Model(inputs=[inputLayer], outputs=[FRFAmpLayer])
functionalModel.compile(
loss='mean_squared_error',
optimizer=Adam(5e-2),
metrics=['mean_absolute_error', 'mean_squared_error'])
return functionalModel
def optimization_ideal(x,y,z,max_iter=3,d = 1):
# _without_intercept
iterations = 0
thisgoodness1 = -np.Inf
iter = []
fit_cost = []
model = None
while iterations<max_iter:
model1 = keras.Sequential([keras.layers.Dense(1, activation='linear', input_dim=1,bias_constraint=MinMaxNorm(min_value=0.0, max_value=0.0, rate=1.0, axis=0))])
selected = random.sample(range(0,len(z)),int(len(z)-3))
x_train = []
y_train = []
z_train = []
z_test = []
x_test = []
y_test = []
for i in range(len(z)):
if i in selected:
x_train.append(x[i])
y_train.append(y[i])
z_train.append(z[i])
else:
z_test.append(z[i])
x_test.append(x[i])
y_test.append(y[i])
x_train = np.array(x_train).reshape(-1,1)
y_train = np.array(y_train).reshape(-1,1)
z_train = np.array(z_train).reshape(-1,1)
x_test = tf.convert_to_tensor(np.array(x_test).reshape(1,-1),dtype=tf.float32)
y_test = tf.convert_to_tensor(np.array(y_test).reshape(1,-1),dtype=tf.float32)
z_test = tf.convert_to_tensor(np.array(z_test).reshape(1,-1),dtype=tf.float32)
def custom_loss(z_test):
def loss(y_test,y_pred):
# return 0.6*(y_test-y_pred)**2 - 0.4*z_test # (for mu-mu)
# return 0.9*(y_test-y_pred)**2 - 0.1*z_test # (for mu-mu)
# return 0.3*(y_test-y_pred)**2 - 0.7*z_test #(for sigma-sigma mu_h=mu_x),(for sigma-sigma mu_h=mu_h_pred)
return 0.2*(y_test-y_pred)**2 - 0.8*z_test #(for sigma-sigma mu_h=mu_x),(for sigma-sigma mu_h=mu_h_pred)
# return 0.05*(y_test-y_pred)**2 - 0.95*z_test #(for sigma-sigma)
return loss
model1.compile(loss=custom_loss(z_test), optimizer='sgd')
model1.fit(x_train, y_train, batch_size=len(x_train), epochs=1000)
set = range(len(x))
for point1 in np.setdiff1d(set,selected):
dis = abs(model1.get_weights()[0][0][0]*x[point1]-y[point1]+model1.get_weights()[1][0])/((model1.get_weights()[0][0][0]*model1.get_weights()[0][0][0] + 1)**0.5)
if dis<=d:
np.append(x_train,x[point1])
np.append(y_train,y[point1])
np.append(z_train,z[point1])
thisgoodness = sum(z_train)/len(z_train)
iterations += 1
if thisgoodness[0]>thisgoodness1:
thisgoodness1 = thisgoodness[0]
iter.append(iterations)
fit_cost.append(thisgoodness1)
model = model1
bestFit = [model.get_weights()[0][0][0],model.get_weights()[1][0]]
return [bestFit,np.round(thisgoodness1,decimals=3),iter,fit_cost]
