def __init__(self, vocab_size, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
#rn = np.radom.randn
# 重みの初期化
embed_W = (rn(V, D) / 100).astype('f')
rnn_Wx = (rn(D, H) / np.sqrt(D)).astype('f')
rnn_Wh = (rn(H, H) / np.sqrt(H)).astype('f')
rnn_b = np.zeros(H).astype('f')
affine_W = (rn(H, V) / np.sqrt(H)).astype('f')
affine_b = np.zeros(V).astype('f')
# レイヤの生成
self.layers = [
TimeEmbedding(embed_W),
TimeRNN(rnn_Wx, rnn_Wh, rnn_b, stateful=True),
TimeAffine(affine_W, affine_b)
]
self.loss_layer = TimeSoftmaxWithLoss()
self.rnn_layer = self.layers[1]
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
def animate(i):
global x, y, vx, vy, dr, con, j, vx_temp, vy_temp
vx_temp, vy_temp = [], []
dr = dr_slider.val
#vx += ((rn(n)-.5)*noise_slider.val)*5
#vy += ((rn(n)-.5)*noise_slider.val)*5
for j in range(n):
con = ((x < x[j] + dr) & (x > x[j] - dr) & (y < y[j] + dr) &
(y > y[j] - dr)) #it indicates if there is someone near or not
if sum(con) != 0:
vx_temp.append(
sum(con * vx) / sum(con) + (rn() - .5) * noise_slider.val)
vy_temp.append(
sum(con * vy) / sum(con) + (rn() - .5) * noise_slider.val)
else:
vx_temp.append(vx[j] + (rn() - .5) / 10)
vy_temp.append(vy[j] + (rn() - .5) / 10)
vx = np.array(vx_temp.copy())
vy = np.array(vy_temp.copy())
x = (x + vx * dt) % w #+(rn(n)-.5)*noise_slider.val
y = (y + vy * dt) % w #+(np.sin(rn(n))-.5)/10
line.set_xdata(x)
line.set_ydata(y)
return line,
def nuevo(self, old, oldE):
# genero nuevo
new = self.sampleador.rvs() # rn(8) * intervalo + cotas[:,0]
self.generados += 1
# cambio de error
newE = self.etotalExt(new)
deltaE = newE - oldE
if deltaE < 0:
self.gradPos += 1
print("Gradiente Positivo")
print(self.generados, self.gradPos, self.gradNeg, self.mismo)
return new, newE # tiene menor error, listo
else:
# nueva opoertunidad, sampleo contra rand
pb = np.exp(-deltaE / 2)
if pb > rn():
self.gradNeg += 1
print("Gradiente Negativo, pb=", pb)
print(self.generados, self.gradPos, self.gradNeg, self.mismo)
return new, newE # aceptado a la segunda oportunidad
else:
# # vuelvo recursivamente al paso2 hasta aceptar
# print('rechazado, pb=', pb)
# new, newE = nuevo(old, oldE)
self.mismo += 1
print("Mismo punto, pb=", pb)
print(self.generados, self.gradPos, self.gradNeg, self.mismo)
return old, oldE
return new, newE
def result():
garr = CArray()
bias = rn()
x = request.values.get('lifex')
y = request.values.get('lifey')
lifeformx = request.values.get('lifeformx')
lifeformy = request.values.get('lifeformy')
nbx = request.values.get('nbx')
nby = request.values.get('nby')
n0x = request.values.get('n0x')
n0y = request.values.get('n0y')
fcx = request.values.get('fcx')
fcy = request.values.get('fcy')
pax = request.values.get('pax')
pay = request.values.get('pay')
resultlifeform = [lifeformx, lifeformy][int(bias)]
garr = CArray(nbx, nby)
nb = garr.get(bias)
garr = CArray(n0x, n0y)
n0 = garr.get(bias)
garr = CArray(fcx, fcy)
fc = garr.get(bias)
garr = CArray(x, y)
resultlife = garr.get(bias)
garr = CArray(pax, pay)
pa = garr.get(bias)
formula = lambda a,b,c,d : a*c*d/b
timeFormula = lambda x,a,b,c,d : formula(a,b,c,d)*x
resultingTime = float(0)
while(timeFormula(resultingTime,nb,n0,fc,pa)<0.5):
resultingTime+=0.01
arr = [int("1") for i in range(int(resultlife))]+[int("0") for i in range(100-int(resultlife))]
populated = False
if numpy.random.choice(arr):
populated = True
data = {"Nb":nb, "N0":n0, "Fc":fc, "Pa":pa, "Time":resultingTime, "LifeProb":resultlife, "LifeForm":resultlifeform, "LifeExpect":formula(nb, n0, fc, pa), "Populated":populated}
return jsonify(data)
def set_camera_pose(dim_height, dim_width, camera_pose, camera_deg):
"""Set up the camera pose, potentially applying domain randomization.
Defaults for the top-down view are 0.5, 0.5, and 1.5 for x, y, and z,
respectively. Defaults for rotations are 0 (radians) for all three angles.
I think yaw, pitch, and roll but the ordering can be determined by testing
values, e.g.: https://github.com/BerkeleyAutomation/gym-cloth/issues/28.
"""
# Select the camera and make it the active object so that we can manipulate it
bpy.data.objects['Camera'].select = True
bpy.context.scene.objects.active = bpy.data.objects['Camera']
# https://blender.stackexchange.com/questions/86233/blender-resizing-my-image-in-half
bpy.data.scenes['Scene'].render.resolution_percentage = 100.0
bpy.context.scene.render.resolution_x = dim_width
bpy.context.scene.render.resolution_y = dim_height
# Ryan: fixed DR
if ADD_DOM_RAND:
cp = [float(i) for i in camera_pose.split(",")]
cd = [float(i) for i in camera_deg.split(",")]
else:
cp = [rn(0., scale=EPS), rn(0., scale=EPS), rn(0., scale=EPS)]
cd = [rn(0., scale=EPS), rn(0., scale=EPS), rn(0., scale=EPS)]
# Set the x, y and z location (Top-down view). Daniel: height was 1.5 but let's do 1.45.
bpy.context.object.location[0] = 0.5 + cp[0]
bpy.context.object.location[1] = 0.5 + cp[1]
bpy.context.object.location[2] = 1.45 + cp[2]
# Set the x, y and z rotation (Top-down view).
bpy.context.object.rotation_euler[0] = DEG_TO_RAD * (0 + cd[0])
bpy.context.object.rotation_euler[1] = DEG_TO_RAD * (0 + cd[1])
bpy.context.object.rotation_euler[2] = DEG_TO_RAD * (0 + cd[2])
def nuevo(oldInt, oldExt, oldErr, retPb=False):
global generados
global gradPos
global gradNeg
global mismo
global sampleador
# genero nuevo
newInt = sampleadorInt.rvs() # rn(8) * intervalo + cotas[:,0]
newExt = sampleadorExt.rvs()
generados += 1
# cambio de error
newErr = etotal(newInt, Ns, newExt, params)
deltaErr = newErr - oldErr
if deltaErr < 0:
gradPos += 1
print(generados, gradPos, gradNeg, mismo, "Gradiente Positivo")
if retPb:
return newInt, newExt, newErr, 1.0
return newInt, newExt, newErr # tiene menor error, listo
else:
# nueva oportunidad, sampleo contra rand
pb = np.exp(-deltaErr / 2)
if pb > rn():
gradNeg += 1
print(generados, gradPos, gradNeg, mismo,
"Gradiente Negativo, pb=", pb)
if retPb:
return newInt, newExt, newErr, pb
return newInt, newExt, newErr # aceptado a la segunda oportunidad
else:
# # vuelvo recursivamente al paso2 hasta aceptar
# print('rechazado, pb=', pb)
# new, newE = nuevo(old, oldE)
mismo += 1
print(generados, gradPos, gradNeg, mismo,
"Mismo punto, pb=", pb)
if retPb:
return oldInt, oldExt, oldErr, pb
return oldInt, oldExt, oldErr
def nuevo(oldInt, oldExt, oldErr, retPb=False):
global generados
global gradPos
global gradNeg
global mismo
global sampleador
# genero nuevo
newInt = sampleadorInt.rvs() # rn(8) * intervalo + cotas[:,0]
newExt = sampleadorExt.rvs()
generados += 1
# cambio de error
newErr = etotal(newInt, Ns, newExt, params)
deltaErr = newErr - oldErr
if deltaErr < 0:
gradPos += 1
print(generados, gradPos, gradNeg, mismo, "Gradiente Positivo")
if retPb:
return newInt, newExt, newErr, 1.0
return newInt, newExt, newErr # tiene menor error, listo
else:
# nueva oportunidad, sampleo contra rand
pb = np.exp(- deltaErr / 2)
if pb > rn():
gradNeg += 1
print(generados, gradPos, gradNeg, mismo, "Gradiente Negativo, pb=", pb)
if retPb:
return newInt, newExt, newErr, pb
return newInt, newExt, newErr # aceptado a la segunda oportunidad
else:
# # vuelvo recursivamente al paso2 hasta aceptar
# print('rechazado, pb=', pb)
# new, newE = nuevo(old, oldE)
mismo +=1
print(generados, gradPos, gradNeg, mismo, "Mismo punto, pb=", pb)
if retPb:
return oldInt, oldExt, oldErr, pb
return oldInt, oldExt, oldErr
def nuevo(old, oldE):
global generados
global gradPos
global gradNeg
global mismo
global sampleador
# genero nuevo
new = sampleador.rvs() # rn(8) * intervalo + cotas[:,0]
generados += 1
# cambio de error
newE = etotal(new, Ns, XextList, params)
deltaE = newE - oldE
if deltaE < 0:
gradPos += 1
print("Gradiente Positivo")
print(generados, gradPos, gradNeg, mismo)
return new, newE # tiene menor error, listo
else:
# nueva opoertunidad, sampleo contra rand
pb = np.exp(-deltaE / 2)
if pb > rn():
gradNeg += 1
print("Gradiente Negativo, pb=", pb)
print(generados, gradPos, gradNeg, mismo)
return new, newE # aceptado a la segunda oportunidad
else:
# # vuelvo recursivamente al paso2 hasta aceptar
# print('rechazado, pb=', pb)
# new, newE = nuevo(old, oldE)
mismo += 1
print("Mismo punto, pb=", pb)
print(generados, gradPos, gradNeg, mismo)
return old, oldE
return new, newE
def nuevo(old, oldE):
global generados
global gradPos
global gradNeg
global mismo
global sampleador
# genero nuevo
new = sampleador.rvs() # rn(8) * intervalo + cotas[:,0]
generados += 1
# cambio de error
newE = etotal(new, Ns, XextList, params)
deltaE = newE - oldE
if deltaE < 0:
gradPos += 1
print("Gradiente Positivo")
print(generados, gradPos, gradNeg, mismo)
return new, newE # tiene menor error, listo
else:
# nueva opoertunidad, sampleo contra rand
pb = np.exp(- deltaE / 2)
if pb > rn():
gradNeg += 1
print("Gradiente Negativo, pb=", pb)
print(generados, gradPos, gradNeg, mismo)
return new, newE # aceptado a la segunda oportunidad
else:
# # vuelvo recursivamente al paso2 hasta aceptar
# print('rechazado, pb=', pb)
# new, newE = nuevo(old, oldE)
mismo +=1
print("Mismo punto, pb=", pb)
print(generados, gradPos, gradNeg, mismo)
return old, oldE
return new, newE
first_row = ['W_speed', 'S1', 'S2', 'S3', 'waveX', 'waveY']
guardar = input('Do you want to save? [(S)/N]\t\t')
# guardar='n'
if guardar.lower() != 'n':
print('\t\tGuardando...')
import os.path
if os.path.exists(file_name) == False:
with open(file_name, 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow(first_row)
else:
print(first_row)
for i in range(num_valores):
if change_speed == True:
speed = rn(10, 25)
activation = []
control = []
continuar = True
if num_valores == 1:
x, y = 450, 250
else:
x = rn(screen[0] * 2 / 3, screen[0])
y = rn(screen[1] / 2, screen[1])
radio = 0
for comparate in range(10 * speed):
radio += 1 * speed
cont = 0
for sensor in positions.values():
cont += 1
distance = int(np.sqrt((x - sensor[0])**2 + (y - sensor[1])**2))
# import statements
import datetime as dt
import pandas as pd
from numpy.random import randn as rn
# initialization
start_date = dt.datetime(2015,1,1).date()
end_date = dt.datetime(2015,12,31).date()
no_of_days = (end_date - start_date).days + 1
# build date collection
dates = [(start_date + dt.timedelta(days = i)).isoformat() for i in range(no_of_days) if not (start_date + dt.timedelta(days = i)).isoweekday() in [6,7]]
# build random numbers series
randoms = rn(len(dates))
random_series = pd.Series(randoms, name = "random_number")
# create data frame
df = pd.DataFrame({"Date": dates})
# join the series and data frame to build the desired output
df = df.join(random_series)
df.set_index("Date", inplace=True)
print(df)
import pandas as pd
import numpy as np
from pathlib import Path
my_path = Path('C:\\Users\\proy\\OneDrive - Tractor Supply Co\\PRAT_TSC\\MISC\\PYTHON'
'\\Krish_Naik\\materials\\files\\')
print(my_path)
from numpy.random import randn as rn # function generate random data
# numpy only understand array or matrix
print(rn(5, 4))
np.random.seed(100) # by setting seed, for same seed , it will generate same set of random data
r = rn(5, 4)
print(r)
pd.DataFrame(r) # convert matrix to dataframe
# passing list variable as row and column names
row = ['a', 'b', 'c', 'd', 'e']
column = ['p', 'q', 'r', 's']
pd.DataFrame(r, index=row, columns=column)
df = pd.DataFrame(r, index=row, columns=column)
df[['p']]
df['new'] = df['p'] + df['s']
df
df.drop('p') # not found in Axis, by default axis = 0
