Python numpy.alen() Examples
The following are 30 code examples for showing how to use numpy.alen(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
| Project: FitML Author: FitMachineLearning File: ROBOTIC_Template_Experimental_v0.1.py License: MIT License | 6 votes |
|
def add_noise_to_model(targetModel,largeNoise = False):
#noisy_model = keras.models.clone_model(action_predictor_model)
#noisy_model.set_weights(action_predictor_model.get_weights())
#print("Adding Noise to actor")
#largeNoise = last_game_average < memoryR.mean()
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = targetModel.layers[k].get_weights()
if np.alen(w) >0 :
#print("k==>",k)
w[0] = add_noise(w[0],largeNoise)
targetModel.layers[k].set_weights(w)
return targetModel
Example 2
| Project: FitML Author: FitMachineLearning File: ROBOTIC_Template_Experimental_v0.1.py License: MIT License | 6 votes |
|
def reset_noisy_model():
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = noisy_model.layers[k].get_weights()
apW = action_predictor_model.layers[k].get_weights()
if np.alen(w) >0:
w[0] = reset_noisy_model_weights_to_apWeights(apW[0])
noisy_model.layers[k].set_weights(w)
#print("w",w)
#print("apW",apW)
# --- Parameter Noising
Example 3
| Project: FitML Author: FitMachineLearning File: Walker2D.v2.0.py License: MIT License | 6 votes |
|
def add_noise_to_model(largeNoise = False):
#noisy_model = keras.models.clone_model(action_predictor_model)
#noisy_model.set_weights(action_predictor_model.get_weights())
#print("Adding Noise to actor")
#largeNoise = last_game_average < memoryR.mean()
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = noisy_model.layers[k].get_weights()
#print("w ==>", w)
if np.alen(w) >0:
w[0] = add_noise_simple(w[0],largeNoise)
noisy_model.layers[k].set_weights(w)
return noisy_model
# --- Parameter Noising
Example 4
| Project: FitML Author: FitMachineLearning File: AntBulletEnv.py License: MIT License | 6 votes |
|
def add_noise_to_model(targetModel,largeNoise = False):
#noisy_model = keras.models.clone_model(action_predictor_model)
#noisy_model.set_weights(action_predictor_model.get_weights())
#print("Adding Noise to actor")
#largeNoise = last_game_average < memoryR.mean()
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = targetModel.layers[k].get_weights()
if np.alen(w) >0 :
#print("k==>",k)
if USE_GAUSSIAN_NOISE:
w[0] = add_gaussian_noise(w[0],big_sigma,largeNoise)
else:
w[0] = add_noise_simple(w[0],largeNoise)
targetModel.layers[k].set_weights(w)
return targetModel
Example 5
| Project: FitML Author: FitMachineLearning File: BipedalWalker_v3.0.py License: MIT License | 6 votes |
|
def add_noise_to_model(targetModel,largeNoise = False):
#noisy_model = keras.models.clone_model(action_predictor_model)
#noisy_model.set_weights(action_predictor_model.get_weights())
#print("Adding Noise to actor")
#largeNoise = last_game_average < memoryR.mean()
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = targetModel.layers[k].get_weights()
if np.alen(w) >0 :
#print("k==>",k)
if USE_GAUSSIAN_NOISE:
w[0] = add_gaussian_noise(w[0],big_sigma,largeNoise)
else:
w[0] = add_noise_simple(w[0],largeNoise)
targetModel.layers[k].set_weights(w)
return targetModel
Example 6
| Project: FitML Author: FitMachineLearning File: Hopper_v1.0.py License: MIT License | 6 votes |
|
def add_noise_to_model(largeNoise = False):
#noisy_model = keras.models.clone_model(action_predictor_model)
#noisy_model.set_weights(action_predictor_model.get_weights())
#print("Adding Noise to actor")
#largeNoise = last_game_average < memoryR.mean()
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = noisy_model.layers[k].get_weights()
#print("w ==>", w)
if np.alen(w) >0:
w[0] = add_noise_simple(w[0],largeNoise)
noisy_model.layers[k].set_weights(w)
return noisy_model
# --- Parameter Noising
Example 7
| Project: FitML Author: FitMachineLearning File: MainAlgo_PR_v1.0.py License: MIT License | 6 votes |
|
def add_noise_to_model(targetModel,largeNoise = False):
#noisy_model = keras.models.clone_model(action_predictor_model)
#noisy_model.set_weights(action_predictor_model.get_weights())
#print("Adding Noise to actor")
#largeNoise = last_game_average < memoryR.mean()
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = targetModel.layers[k].get_weights()
if np.alen(w) >0 :
#print("k==>",k)
if USE_GAUSSIAN_NOISE:
w[0] = add_gaussian_noise(w[0],big_sigma,largeNoise)
else:
w[0] = add_noise_simple(w[0],largeNoise)
targetModel.layers[k].set_weights(w)
return targetModel
Example 8
| Project: FitML Author: FitMachineLearning File: RoboschoolHalfCheetah_v1.py License: MIT License | 6 votes |
|
def add_noise_to_model(targetModel,largeNoise = False):
#noisy_model = keras.models.clone_model(action_predictor_model)
#noisy_model.set_weights(action_predictor_model.get_weights())
#print("Adding Noise to actor")
#largeNoise = last_game_average < memoryR.mean()
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = targetModel.layers[k].get_weights()
if np.alen(w) >0 :
#print("k==>",k)
w[0] = add_noise(w[0],largeNoise)
targetModel.layers[k].set_weights(w)
return targetModel
Example 9
| Project: FitML Author: FitMachineLearning File: _MainAlgo_v4.2.py License: MIT License | 6 votes |
|
def add_noise_to_model(targetModel,largeNoise = False):
#noisy_model = keras.models.clone_model(action_predictor_model)
#noisy_model.set_weights(action_predictor_model.get_weights())
#print("Adding Noise to actor")
#largeNoise = last_game_average < memoryR.mean()
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = targetModel.layers[k].get_weights()
if np.alen(w) >0 :
#print("k==>",k)
if USE_GAUSSIAN_NOISE:
w[0] = add_gaussian_noise(w[0],big_sigma,largeNoise)
else:
w[0] = add_noise_simple(w[0],largeNoise)
targetModel.layers[k].set_weights(w)
return targetModel
Example 10
| Project: FitML Author: FitMachineLearning File: Main_algo.py License: MIT License | 6 votes |
|
def add_noise_to_model(targetModel,largeNoise = False):
#noisy_model = keras.models.clone_model(action_predictor_model)
#noisy_model.set_weights(action_predictor_model.get_weights())
#print("Adding Noise to actor")
#largeNoise = last_game_average < memoryR.mean()
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = targetModel.layers[k].get_weights()
if np.alen(w) >0 :
#print("k==>",k)
w[0] = add_noise(w[0],largeNoise)
targetModel.layers[k].set_weights(w)
return targetModel
Example 11
| Project: FitML Author: FitMachineLearning File: Main_algo.py License: MIT License | 6 votes |
|
def reset_noisy_model():
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = noisy_model.layers[k].get_weights()
apW = action_predictor_model.layers[k].get_weights()
if np.alen(w) >0:
w[0] = reset_noisy_model_weights_to_apWeights(apW[0])
noisy_model.layers[k].set_weights(w)
#print("w",w)
#print("apW",apW)
# --- Parameter Noising
Example 12
| Project: FitML Author: FitMachineLearning File: LunarLanderContinuous_v1.0.py License: MIT License | 6 votes |
|
def add_noise_to_model(targetModel,largeNoise = False):
#noisy_model = keras.models.clone_model(action_predictor_model)
#noisy_model.set_weights(action_predictor_model.get_weights())
#print("Adding Noise to actor")
#largeNoise = last_game_average < memoryR.mean()
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = targetModel.layers[k].get_weights()
if np.alen(w) >0 :
#print("k==>",k)
w[0] = add_noise(w[0],largeNoise)
targetModel.layers[k].set_weights(w)
return targetModel
Example 13
| Project: FitML Author: FitMachineLearning File: LunarLanderContinuous_v1.0.py License: MIT License | 6 votes |
|
def reset_noisy_model():
sz = len(noisy_model.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = noisy_model.layers[k].get_weights()
apW = action_predictor_model.layers[k].get_weights()
if np.alen(w) >0:
w[0] = reset_noisy_model_weights_to_apWeights(apW[0])
noisy_model.layers[k].set_weights(w)
#print("w",w)
#print("apW",apW)
# --- Parameter Noising
Example 14
| Project: recruit Author: Frank-qlu File: fromnumeric.py License: Apache License 2.0 | 5 votes |
|
def alen(a):
"""
Return the length of the first dimension of the input array.
Parameters
----------
a : array_like
Input array.
Returns
-------
alen : int
Length of the first dimension of `a`.
See Also
--------
shape, size
Examples
--------
>>> a = np.zeros((7,4,5))
>>> a.shape[0]
7
>>> np.alen(a)
7
"""
try:
return len(a)
except TypeError:
return len(array(a, ndmin=1))
Example 15
| Project: lambda-packs Author: ryfeus File: fromnumeric.py License: MIT License | 5 votes |
|
def alen(a):
"""
Return the length of the first dimension of the input array.
Parameters
----------
a : array_like
Input array.
Returns
-------
alen : int
Length of the first dimension of `a`.
See Also
--------
shape, size
Examples
--------
>>> a = np.zeros((7,4,5))
>>> a.shape[0]
7
>>> np.alen(a)
7
"""
try:
return len(a)
except TypeError:
return len(array(a, ndmin=1))
Example 16
| Project: auto-alt-text-lambda-api Author: abhisuri97 File: fromnumeric.py License: MIT License | 5 votes |
|
def alen(a):
"""
Return the length of the first dimension of the input array.
Parameters
----------
a : array_like
Input array.
Returns
-------
alen : int
Length of the first dimension of `a`.
See Also
--------
shape, size
Examples
--------
>>> a = np.zeros((7,4,5))
>>> a.shape[0]
7
>>> np.alen(a)
7
"""
try:
return len(a)
except TypeError:
return len(array(a, ndmin=1))
Example 17
| Project: vnpy_crypto Author: birforce File: fromnumeric.py License: MIT License | 5 votes |
|
def alen(a):
"""
Return the length of the first dimension of the input array.
Parameters
----------
a : array_like
Input array.
Returns
-------
alen : int
Length of the first dimension of `a`.
See Also
--------
shape, size
Examples
--------
>>> a = np.zeros((7,4,5))
>>> a.shape[0]
7
>>> np.alen(a)
7
"""
try:
return len(a)
except TypeError:
return len(array(a, ndmin=1))
Example 18
| Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: fromnumeric.py License: MIT License | 5 votes |
|
def alen(a):
"""
Return the length of the first dimension of the input array.
Parameters
----------
a : array_like
Input array.
Returns
-------
alen : int
Length of the first dimension of `a`.
See Also
--------
shape, size
Examples
--------
>>> a = np.zeros((7,4,5))
>>> a.shape[0]
7
>>> np.alen(a)
7
"""
try:
return len(a)
except TypeError:
return len(array(a, ndmin=1))
Example 19
| Project: PYRO-NN Author: csyben File: filters.py License: Apache License 2.0 | 5 votes |
|
def ramp_2D(geometry):
detector_width = geometry.detector_shape[np.alen(geometry.detector_shape) - 1]
filter = [
np.reshape(
ramp(detector_width),
(1, detector_width)
)
for i in range(0, geometry.number_of_projections)
]
filter = np.concatenate(filter)
return filter
Example 20
| Project: PYRO-NN Author: csyben File: filters.py License: Apache License 2.0 | 5 votes |
|
def ramp_3D(geometry):
detector_width = geometry.detector_shape[np.alen(geometry.detector_shape) - 1]
filter = [
np.reshape(
ramp(detector_width),
(1, 1, detector_width)
)
for i in range(0, geometry.number_of_projections)
]
filter = np.concatenate(filter)
return filter
Example 21
| Project: PYRO-NN Author: csyben File: filters.py License: Apache License 2.0 | 5 votes |
|
def ram_lak_2D(geometry):
detector_width = geometry.detector_shape[np.alen(geometry.detector_shape) - 1]
detector_spacing_width = geometry.detector_spacing[np.alen(geometry.detector_spacing) - 1]
filter = [
np.reshape(
ram_lak(detector_width, detector_spacing_width),
(1, detector_width)
)
for i in range(0, geometry.number_of_projections)
]
filter = np.concatenate(filter)
return filter
Example 22
| Project: PYRO-NN Author: csyben File: filters.py License: Apache License 2.0 | 5 votes |
|
def ram_lak_3D(geometry):
detector_width = geometry.detector_shape[np.alen(geometry.detector_shape) - 1]
detector_spacing_width = geometry.detector_spacing[np.alen(geometry.detector_spacing) - 1]
filter = [
np.reshape(
ram_lak(detector_width, detector_spacing_width),
(1, 1, detector_width)
)
for i in range(0, geometry.number_of_projections)
]
filter = np.concatenate(filter)
return (1 / 1.0) * filter
Example 23
| Project: GraphicDesignPatternByPython Author: Relph1119 File: fromnumeric.py License: MIT License | 5 votes |
|
def alen(a):
"""
Return the length of the first dimension of the input array.
Parameters
----------
a : array_like
Input array.
Returns
-------
alen : int
Length of the first dimension of `a`.
See Also
--------
shape, size
Examples
--------
>>> a = np.zeros((7,4,5))
>>> a.shape[0]
7
>>> np.alen(a)
7
"""
try:
return len(a)
except TypeError:
return len(array(a, ndmin=1))
Example 24
| Project: predictive-maintenance-using-machine-learning Author: awslabs File: fromnumeric.py License: Apache License 2.0 | 5 votes |
|
def alen(a):
"""
Return the length of the first dimension of the input array.
Parameters
----------
a : array_like
Input array.
Returns
-------
alen : int
Length of the first dimension of `a`.
See Also
--------
shape, size
Examples
--------
>>> a = np.zeros((7,4,5))
>>> a.shape[0]
7
>>> np.alen(a)
7
"""
try:
return len(a)
except TypeError:
return len(array(a, ndmin=1))
Example 25
| Project: pySINDy Author: luckystarufo File: fromnumeric.py License: MIT License | 5 votes |
|
def alen(a):
"""
Return the length of the first dimension of the input array.
Parameters
----------
a : array_like
Input array.
Returns
-------
alen : int
Length of the first dimension of `a`.
See Also
--------
shape, size
Examples
--------
>>> a = np.zeros((7,4,5))
>>> a.shape[0]
7
>>> np.alen(a)
7
"""
try:
return len(a)
except TypeError:
return len(array(a, ndmin=1))
Example 26
| Project: FitML Author: FitMachineLearning File: ROBOTIC_Template_Experimental_v0.1.py License: MIT License | 5 votes |
|
def scale_weights(memR,memW):
rmax = memR.max()
rmin = memR.min()
reward_range = math.fabs(rmax - rmin )
if reward_range == 0:
reward_range = 10
for i in range(np.alen(memR)):
memW[i][0] = math.fabs(memR[i][0]-rmin)/reward_range
memW[i][0] = max(memW[i][0],0.001)
#print("memW %5.2f reward %5.2f rmax %5.2f rmin %5.2f "%(memW[i][0],memR[i][0],rmax,rmin))
#print("memW",memW)
return memW
Example 27
| Project: FitML Author: FitMachineLearning File: ROBOTIC_Template_Experimental_v0.1.py License: MIT License | 5 votes |
|
def pr_actor_experience_replay(memSA,memR,memS,memA,memW,num_epochs=1):
tSA = (memSA)
tR = (memR)
tX = (memS)
tY = (memA)
tW = (memW)
tX_train = np.zeros(shape=(1,num_env_variables))
tY_train = np.zeros(shape=(1,num_env_actions))
for i in range(np.alen(tR)):
pr = predictTotalRewards(tX[i],GetRememberedOptimalPolicy(tX[i]))
#print ("tR[i]",tR[i],"pr",pr)
d = math.fabs( memoryR.max() - pr)
tW[i]= 0.0000000000000005
if (tR[i]>pr):
tW[i]=0.15
if (tR[i]>pr+d/2):
tW[i] = 1
if tW[i]> np.random.rand(1):
tX_train = np.vstack((tX_train,tX[i]))
tY_train = np.vstack((tY_train,tY[i]))
tX_train = tX_train[1:]
tY_train = tY_train[1:]
print("%8d were better After removing first element"%np.alen(tX_train))
if np.alen(tX_train)>0:
action_predictor_model.fit(tX_train,tY_train, batch_size=mini_batch, nb_epoch=num_epochs,verbose=0)
Example 28
| Project: FitML Author: FitMachineLearning File: ROBOTIC_Template_Experimental_v0.1.py License: MIT License | 5 votes |
|
def train_noisy_actor():
tX = (memoryS)
tY = (memoryA)
tW = (memoryW)
train_A = np.random.randint(tY.shape[0],size=int(min(experience_replay_size,np.alen(tY) )))
tX = tX[train_A,:]
tY = tY[train_A,:]
tW = tW[train_A,:]
noisy_model.fit(tX,tY, batch_size=mini_batch, nb_epoch=training_epochs,verbose=0)
Example 29
| Project: FitML Author: FitMachineLearning File: Walker2D.v2.0.py License: MIT License | 5 votes |
|
def scale_weights(memR,memW):
rmax = memR.max()
rmin = memR.min()
reward_range = math.fabs(rmax - rmin )
if reward_range == 0:
reward_range = 10
for i in range(np.alen(memR)):
memW[i][0] = math.fabs(memR[i][0]-rmin)/reward_range
memW[i][0] = max(memW[i][0],0.001)
#print("memW %5.2f reward %5.2f rmax %5.2f rmin %5.2f "%(memW[i][0],memR[i][0],rmax,rmin))
#print("memW",memW)
return memW
Example 30
| Project: FitML Author: FitMachineLearning File: Walker2D.v2.0.py License: MIT License | 5 votes |
|
def train_noisy_actor():
tX = (memoryS)
tY = (memoryA)
tW = (memoryW)
train_A = np.random.randint(tY.shape[0],size=int(min(experience_replay_size,np.alen(tY) )))
tX = tX[train_A,:]
tY = tY[train_A,:]
tW = tW[train_A,:]
noisy_model.fit(tX,tY, batch_size=mini_batch, nb_epoch=training_epochs,verbose=0)
