def american(n, T, sigma, s_nil, K): dt = T*252 u = random.randn(dt)* sigma/sqrt(dt) z = cumprod(1+random.randn(n,dt)*(sigma/sqrt(dt), 1))*s_nil payoffs = (z[:,-1] - 100) * ((z[:,-1] - 100) > 0) price = mean(payoffs) print(payoffs, price)
def __init__(self, layers, l_rate, mu = 0, sigma = 0.01):
self.depth = len(layers)
self.l_rate = l_rate
self.layers = [ mat([0 for items in range(layers[row])], dtype = float) for row in range(self.depth)]
self.bias = [ mat([sigma*random.randn() for items in range(layers[row])], dtype = float) for row in range(self.depth)]
self.b_grads = [ mat([0 for items in range(layers[row])], dtype = float) for row in range(self.depth)]
self.weights = [ mat([ [sigma*random.randn() for z in range(layers[x+1])] for y in range(layers[x])], dtype = float) for x in range(self.depth-1)]
self.w_grads = [ mat([ [0 for z in range(layers[x])] for y in range(layers[x+1])], dtype = float) for x in range(self.depth-1)]
self.label = 0
def serie(size, freq, factor=None, phase=None):
noise = .01 * random.randn(ts.shape[0])
if freq is None: return np.abs(fft.fft(noise, size))
if factor is None: factor = 1 + .02 * (random.rand() - .5)
if phase is None: phase = 2j * np.pi * random.rand()
freq_ = factor * freq
ys = np.exp(2j * np.pi * freq_ * ts - phase) + noise
return np.abs(fft.fft(ys, size))
def makePeriodSeries(nper=10, name=None, freq="B", dtype="int64", start=None):
if dtype == "int64":
data = [random.randint(0, 100) for i in range(nper)]
elif dtype == "float64":
data = random.randn(nper)
elif dtype == 'bool':
data = random.randn(nper) < 0
elif dtype == 'categorical':
cat = {0: "zero", 1: "one", 2: "two"}
data = pd.Categorical([random.randint(0, 3) for i in range(10)],
categories=cat,
ordered=True)
else:
raise ValueError("unsupported dtype, {}".format(dtype))
return pd.Series(data,
index=makePeriodIndex(nper, freq=freq, start=start),
name=name)
def __init__(self, input_size, output_size, l_rate, myrank = 0, mu = 0, sigma = 0.01):
#inputs and outputs
self.inputs = mat([0 for item in range(input_size)], dtype=float)
self.outs = mat([0 for item in range(output_size)], dtype=float)
#parameters
self.bias = mat([0 for item in range(output_size)], dtype=float)
self.weights = mat([[0 for col in range(output_size)] for row in range(input_size)], dtype=float)
#gradients of parameters
self.b_grads = mat([0 for item in range(output_size)], dtype=float)
self.w_grads = mat([[0 for col in range(output_size)] for row in range(input_size)], dtype=float)
#records of vector sizes
self.int_sz = input_size
self.out_sz = output_size
#record label of current input
self.label = 0
#record testing data
self.accepted = 0
self.count = 0
self.record = range(10)
#learning rate
self.l_rate = l_rate
#parameters for initialize biases and weights
self.mu = mu
self.sigma = sigma
'''----MPI----'''
#MPI rank
self.rank = myrank
'''----MPI----'''
#initialize biases and weights
for i in range(output_size):
self.bias[0, i] = sigma*random.randn()
for i in range(0, input_size):
for j in range(0, output_size):
self.weights[i, j] = sigma*random.randn()
def reset_network(n1=6, n2=7, random=np.random):
global W1, W2, W3, b1, b2, b3
W1 = random.randn(n1, 1) / 2
W2 = random.randn(n2, n1) / 2
W3 = random.randn(2, n2) / 2
b1 = random.randn(n1, 1) / 2
b2 = random.randn(n2, 1) / 2
b3 = random.randn(2, 1) / 2
def monte_carlo_simulator(num, ttm, vol, price, strike):
delta = ttm * 252
data_points = random.randn(delta) * vol / sqrt(delta)
plt.hist(data_points)
plt.title("Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")
random_walk = cumprod(1 + random.randn(num, delta) * vol / sqrt(delta), 1) * price
plt.plot(random_walk)
plt.show()
plt.title("Geometric Random Walk")
plt.xlabel("Time")
plt.ylabel("Stock Price")
for point in random_walk:
plt.plot(point)
plt.show()
plt.hist(random_walk[:,-1],40)
plt.show()
option_payoff = (random_walk[:,-1] - 100) * ((random_walk[:,-1] - 100) > 0)
price = mean(option_payoff)
print(price)
def inv_speckle_noise(image, sigma=0.5):
lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
gray, a, b = cv2.split(lab)
gray = gray.astype(np.float32) / 255
H, W = gray.shape
noise = sigma * random.randn(H, W)
noise = np.array([random.random() for i in range(H * W)])
noise = noise.reshape(H, W)
noisy = gray + (1 - gray) * noise
noisy = (np.clip(noisy, 0, 1) * 255).astype(np.uint8)
lab = cv2.merge((noisy, a, b))
image = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
return image
def main_optimal():
x0 = random.randn(2)
x0_g = random.rand(2)
start_time = time.time()
x_min = fmin(neg_f, x0)
print(type((x_min)))
delta = 3
x_glob = basinhopping(neg_f, x0_g)
stop_time = time.time()
print('time', stop_time - start_time)
# print(stop_time-start_time)
x_glob_p = x_glob['x']
x_knots = linspace(x_min[0] - delta, x_min[0] + delta, 41)
y_knots = linspace(x_min[1] - delta, x_min[1] + delta, 41)
X, Y = meshgrid(x_knots, y_knots)
Z = zeros(X.shape)
for i in range(Z.shape[0]):
for j in range(Z.shape[1]):
Z[i][j] = f([X[i, j], Y[i, j]])
ax = Axes3D(figure(figsize=(8, 5)))
ax.plot_surface(X,
Y,
Z,
rstride=1,
cstride=1,
cmap=cm.coolwarm,
linewidth=0.4)
ax.plot([x0[0]], [x0[1]], [f(x0)],
color='g',
marker='o',
markersize=20,
label='initial')
ax.plot([x_min[0]], [x_min[1]], [f(x_min)],
color='k',
marker='o',
markersize=20,
label='lokal')
ax.plot([x_glob['x'][0]], [x_glob['x'][1]], [f(x_glob_p)],
color='b',
marker='o',
markersize=10,
label='glob')
ax.legend()
show()
def x_glob():
x0 = random.randn(2)
x_min = basinhopping(neg_f, x0)
print(x_min)
[0,0,0,1,0,0],
[0,0,0,0,1,0],
[0,0,0,0,0,1]])
BS1 = np.mat([3000, 100, 20])
BS2 = np.mat([200, 3000, 50])
BS3 = np.mat([300, 400, 3000])
BSb = np.mat([450, -200, 100])
dat = 1.35
Q = dat*np.eye(3, dtype = int)
# print('Q=',Q)
TSOA = math.sqrt(10)
TDOA = math.sqrt(10)
# W = np.sqrt(Q)*random.randn(3, 1)
W = np.square(Q)*random.randn(3, 1)
# print('W=',W)
R = np.diag([TDOA, TDOA, TDOA])
# print('R=',R)
G = np.array([[T*T/2.0, 0, 0],
[0, T*T/2.0, 0],
[0, 0, T*T/2.0]])
# G = np.eye(3,dtype = T^2/2) 错误代码语句
# print('G = ',G)
def h_pre(x):
global T, BS1, BS2, BS3, BSb
# 距离公式
h1 = np.zeros((1, 3))
h1[:, 0] = math.sqrt((x[0] - BS1[0])**2 + (x[1] - BS1[1])**2 + (x[2] - BS1[2]**2))
h1[:, 1] = math.sqrt((x[0] - BS2[0])**2 + (x[1] - BS2[1])**2 + (x[2] - BS2[2]**2))
Some packages have multiple *modules*, or sets of related functions, which can be loaded separately. For example, `numpy`'s `random` module contains functions for generating random numbers. from numpy import random You can even import specific functions from packages or modules. For example, `randn` generates numbers from the standard normal distribution. from numpy.random import randn Now each of these three function calls does the same thing: generate 10 random numbers from the standard normal distribution. randn(10) random.randn(10) # Type out the third, using the np prefix from before. # Also try using tab after the dots for code completion and and open parenthesis # for function documentation. # ANSWER: np.random.randn(10) ## Vector operations in `numpy` and `pandas` Vector/array operations are integral to scientific computing in Python. Like `gen` in Stata and `apply` in R, `numpy` and `pandas` include rich sets of vectorized functions to run common code over records quickly. One way to take advantage of this is to pass lists to `numpy` functions; this often produces a new `array` with the same number of elements. np.exp([0, 1, 2])
class BPNN(object):
def __init__（self,sizes):
self.sizes = sizes
self.num_layers = len(sizes)
self.w_ = [random.randn(x,y) for x,y in zip(sizes[1:],sizes[:-1])]
self.b_ = [random.randn(y,1) for y in sizes[1:]]
def random_weight(self):
w = 0.5 + random.randn() / 2
return w
def plot(self, volatilidad, Time_mature, k):
print("aca")
# a figure instance to plot on
self.figure = Figure()
# this is the Canvas Widget that displays the `figure`
# it takes the `figure` instance as a parameter to __init__
self.canvas = FigureCanvas(self.figure)
# this is the Navigation widget
# it takes the Canvas widget and a parent
self.toolbar = NavigationToolbar(self.canvas, self)
# set the layout
layout = QtGui.QVBoxLayout(self.widget_graph)
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
self.setLayout(layout)
# self.addButton = QtGui.QPushButton('button to add other widgets')
# self.mainLayout = QtGui.QVBoxLayout(self.widget_graph)
# self.mainLayout.addWidget(self.addButton)
data = cumprod(
1 + random.randn(1000, int(Time_mature * 252)) *
(volatilidad / sqrt(int(Time_mature * 252))), 1) * k
print(data)
ax = self.figure.add_subplot(111)
ax.clear()
avg = [
sum([subdata[j] for subdata in data]) / len(data)
for j in xrange(len(data[0]))
]
perc95 = percentile(95, data)
perc5 = percentile(5, data)
ax.plot(perc95, 'k- -')
ax.plot(perc5, 'k- -')
ax.plot(avg, 'r-')
#for i in data:
# ax.plot(i, '*-')
self.canvas.draw()
# data = [random.random() for i in range(10)]
# # create an axis
# ax = self.figure.add_subplot(111)
# # discards the old graph
# ax.clear()
# # plot data
# ax.plot(data, '*-')
# # refresh canvas
# self.canvas.draw()
print("alla")
self.show()
f = ImageFont.truetype(font, font_size)
x_offset = (new_size / 8.0 * (random.random() - 0.0))
y_offset = (new_size / 4.0 * (random.random() - 0.0))
n_perline = max(
1,
math.ceil((new_size - 1 * x_offset) / font_size) - 1)
n_lines = math.ceil(1.0 * len(text) / n_perline)
y_delta = int(font_size * 1.5)
for t in range(n_lines):
if t == n_lines - 1:
d.text( (x_offset,y_offset+t*y_delta), \
text[ (0+t*n_perline): ], fill=0, font=f)
else:
d.text( (x_offset,y_offset+t*y_delta), \
text[(0+t*n_perline):((t+1)*n_perline)], fill=0, font=f)
if noisy:
noise = 25.5 * random.randn() * random.randn(
img.size[0], img.size[1])
img = np.asarray(img, dtype=np.float) + noise
img = np.maximum(np.minimum(img, 255), 0)
img = Image.fromarray(img.astype('uint8'), 'L')
img.save(dest_path + str(pic_count).zfill(6) + '.png')
img.close()
pic_count += 1
print('')
[0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1]])
dat = 1.35
r = np.array([1, 1, 1])
G = np.mat([[t**2/2, 0, 0],
[0, t**2/2, 0],
[0, 0, t**2/2],
[t, 0, 0],
[0, t, 0],
[0, 0, t]])
Q = dat*np.diag(r)
TSOA = math.sqrt(10)
TDOA = math.sqrt(10)
random2 = random.randn(3, 1)
W = sqrt(Q)*random2
L = 6
alpha = 0.3#可以调节 改变均值
kalpha = 0.54
belta = 2#对高斯分布通常是2最优100 可以改变方差
lamada = alpha*alpha*(L+kalpha)-L
c = L+lamada
Wm = [lamada/c, 0.5/c, 0.5/c, 0.5/c, 0.5/c, 0.5/c, 0.5/c, 0.5/c, 0.5/c, 0.5/c, 0.5/c, 0.5/c, 0.5/c]
Wc = Wm
Wc[0] = Wc[0]+(1-alpha**2+belta)
c = sqrt(c)
xsP1 = zeros((6, 6))
xsP2 = zeros((6, 6))
xsP11 = zeros((6, 6))
def sample(self):
"""Update internal state and return it as a noise sample."""
x = self.state
dx = self.theta * (self.mu - x) + self.sigma * np.array([random.randn() for i in range(len(x))])
self.state = x + dx
return self.state
# POSSIBILITY OF SUCH DAMAGE.
#
# Revision $Id$
## Simple talker demo that published std_msgs/Strings messages
## to the 'chatter' topic
import rospy
from assignment.msg import iotsensor
from std_msgs.msg import String
import random
pub = rospy.Publisher('iot_sensor_topic', iotsensor, queue_size=10)
rospy.init_node('iot_sensor_publisher_node', anonymous=True)
rate = rospy.Rate(1)
i = 0
while not rospy.is_shutdown():
iot_sensor = iot_sensor()
iot_sensor.id = 1
iot_sensor.name = "iot_parking_01"
iot_sensor.temperature = 24.33 + (random.randn() * 2)
iot_sensor.humidity = 33.4 + (random.randn() * 2)
rospy.loginfo("I publish")
rospy.loginfo(iot_sensor)
pub.publish(iot_sensor)
i = i + 1
rate.sleep()
r = -i - 1
axis_x.append(i)
axis_y_l.append(data[l])
axis_y_r.append(data[r])
plt.plot(axis_x, axis_y_r, label=r"$x_i^{max}(k)$")
plt.plot(axis_x, axis_y_l, label=r"$x_i^{min}(k)$")
plt.xlabel(r"iteration number $k$")
plt.ylabel(r"$x_i^{max}(k)$ and $x_i^{min}(k)$")
plt.legend()
plt.show()
def variance_consensus():
size = 40
data = read_data("data40.txt")
net = Network(new_top=True, num=size, s_dis=40)
net.set_data(data)
net.variance_consensus(max_iter=60)
def generic_pdf():
size = 40
data = read_data("data40.txt")
net = Network(new_top=True, num=size, s_dis=40)
net.set_data(data)
net.generic_pdf_consensus(sections=20, sim=True, max_iter=60)
A = random.randn(4, 3)
B = sum(A, axis=1, keepdims=True)
print(B.shape)
def br_corner():
return random.randn(2) * 100.0 + (SCREEN - array([VISION,VISION]))
def random_corner():
if random.rand() > 0.5:
return random.randn(2) * 10.0 + (SCREEN - array([VISION,VISION]))
else:
return random.randn(2) * 10.0 + (zeros(2) + array([VISION,VISION]))
def fun():
x = random.randn(2) / 10
print(x)
return ((1 + x[0]**2 + x[1]**2))
N = 10001
Nf = 3
t = arange(N, dtype=float)
Ts = random.rand(Nf) * 200 + 100
fs = 1 / Ts
print('The real unknown frequencies are:', fs)
amp = random.rand(Nf) * 200 + 100
phi = random.rand(Nf) * 2 * pi
h = zeros(N)
for j in range(len(fs)):
h += amp[j] * np.sin(2 * pi * t * fs[j] + phi[j])
hn = h + random.randn(N) * 3 * h + random.randn(N) * 700
plt.scatter(t, hn, s=3)
plt.show()
#Frequency Sampling
ind = arange(1, int(N / 2 + 1)) # Sampling of real space
allfreqs = fftfreq(N) # Sampling of frequency space
realfreqs = fftfreq(
N
)[ind] # Frequencies in which we are interested (we omit redundancies due to complex conjugates)
#We now put the fourier transform coefficients
Hn = scipy.fftpack.fft(hn)
plt.plot(allfreqs, Hn)
plt.show()
slpsigma=sigma*.1/sqrt(2)
windsigma=sigma*5./sqrt(2)
cenx=nx*dx/2.
ceny=ny*dy/2.
dz=sqrt(dx**2+dy**2)
ignr=dz*2
historys=runtime
timestep=dz*1.0/6.
ignxsigma=sigma*dx*nx/10.
ignysigma=sigma*dy*ny/10.
seed()
slpx=randn(0,slpsigma)
slpy=randn(0,slpsigma)
windx=randn(0,windsigma)
windy=randn(0,windsigma)
fuel=randint(1,13)
ignx=randn(cenx,ignxsigma)
igny=randn(ceny,ignysigma)
args=['--nx',nx,'--ny',ny,'--dx',dx,'--dy',dy,
'--windx',windx,'--windy',windy,'--slopex',slpx,'--slopey',slpy,
'--fuelcat',fuel,'--timestep',timestep,'--runtime',runtime,'--history',historys,
'--ignx1',ignx,'--ignx2',ignx,'--igny1',igny,'--igny2',igny,
'--ignr',ignr,'--ignt1',igntime,'--ignt2',igntime]
args=[ str(a) for a in args]
def createRun():
file_runlist = open(filename_runlist,'w')
file_truth = open(filename_truth,'w')
truth_header = '# true_si true_hlr true_g1 true_g2 model_si model_hlr model_g1 model_g2 noise_hlr noise_g1 noise_g2\n'
file_truth.write(truth_header)
grid_sersic_index = arange(config['grid']['min'],config['grid']['max']+config['grid']['step'],config['grid']['step'])
file_conf_templ = open(filename_yaml_templ,'r')
conf_templ = file_conf_templ.read()
file_ini_templ = open(filename_ini_templ,'r')
ini_templ = file_ini_templ.read()
logging.info('got %d galaxies' % len(config['gals']))
for iser,ser in enumerate(grid_sersic_index):
ini_filled = ini_templ % (config['n_pix'],ser)
filename_ini = 'si%02d.ini' % iser
filepath_ini = os.path.join(dirname_ini,filename_ini)
file_ini = open(filepath_ini,'w')
file_ini.write(ini_filled)
file_ini.close()
for igal,gal in enumerate(config['gals']):
# get the real galaxy
n_tile=config['n_tile']
n_pix=config['n_pix']
hlr = gal['half_light_radius']
g1 = gal['g1']
g2 = gal['g2']
sersic_index_real = gal['sersic_index']
filename_real = 'real%02d.fits' % (igal)
filepath_real = os.path.join(dirname_images,filename_real)
conf_filled = conf_templ % (sersic_index_real,hlr,g1,g2,n_tile,n_tile,n_pix,n_pix,filepath_real)
filename_conf = 'real%02d.yaml' % igal
filepath_conf = os.path.join(dirname_yaml,filename_conf)
file_conf = open(filepath_conf,'w')
file_conf.write(conf_filled)
file_conf.close()
filename_cmd = 'cmd.sh'
file_cmd = open(filename_cmd,'w')
file_cmd.write('python %s %s\n' % (filepath_galsim_yaml, filepath_conf))
file_cmd.close()
if (not args.reimage) and os.path.isfile(filepath_real):
logging.info('NOT creating %s' % filepath_real)
else:
subprocess.call(('sh',filename_cmd))
# filename_ini = os.path.join(dirname_ini,'si%02d.ini' % ser)
image_tiled = pyfits.getdata(filepath_real)
image_stamp = image_tiled[0:n_pix,0:n_pix]
noise_std = linalg.norm(image_stamp)/config['snr']
img_real = image_tiled
noise_same = []
for nn in range(config['n_reps_diff']):
noise = random.randn(img_real.shape[0],img_real.shape[1])*noise_std
filename_noisy_real_diff = filename_real + ('.d%02d' % nn)
filepath_noisy_real_diff = os.path.join(dirname_images,filename_noisy_real_diff)
img_real_noisy_diff = img_real + noise
if (not args.reimage) and os.path.isfile(filepath_noisy_real_diff):
logging.info('NOT creating noisy images %s' % (filepath_noisy_real_diff))
else:
pyfits.writeto(filepath_noisy_real_diff,img_real_noisy_diff.astype(float32),clobber=True)
for iser,ser in enumerate(grid_sersic_index):
filename_ini = 'si%02d.ini' % iser
filepath_ini = os.path.join(dirname_ini,filename_ini)
file_runlist.write('%s\t%s\n' % (filename_noisy_real_diff,filename_ini))
for nn in range(config['n_reps_same']):
noise_same.append(random.randn(img_real.shape[0],img_real.shape[1])*noise_std)
filename_noisy_real_same = filename_real + ('.s%02d' % nn)
filepath_noisy_real_same = os.path.join(dirname_images,filename_noisy_real_same)
img_real_noisy_same = img_real + noise_same[nn]
if (not args.reimage) and os.path.isfile(filepath_noisy_real_same):
logging.info('NOT creating noisy images %s' % (filepath_noisy_real_same))
else:
pyfits.writeto(filepath_noisy_real_same,img_real_noisy_same.astype(float32),clobber=True)
for iser,ser in enumerate(grid_sersic_index):
filename_ini = 'si%02d.ini' % iser
filepath_ini = os.path.join(dirname_ini,filename_ini)
file_runlist.write('%s\t%s\n' % (filename_noisy_real_same,filename_ini))
# now create the bestfit images
for iser,ser in enumerate(grid_sersic_index):
# this should have been created already
filename_ini = 'si%02d.ini' % iser
filepath_ini = os.path.join(dirname_ini,filename_ini)
logging.info('running im3shape')
i3gal = getBestFit(image_stamp,filepath_ini)
ser_g1 = i3gal.params_gal_measured[2]
ser_g2 = 0
ser_hlr = i3gal.params_gal_measured[4]
truth_line = '%2.2f\t%2.8f\t% 2.8f\t% 2.8f\t%2.2f\t%2.8f\t% 2.8f\t% 2.8f\n' % (sersic_index_real,hlr,g1,g2,ser,ser_hlr,ser_g1,ser_g2)
logging.info(truth_line)
file_truth.write(truth_line)
filename_bfit = 'real%02d.bfit%02d.fits' % (igal,iser)
filepath_bfit = os.path.join(dirname_images,filename_bfit)
conf_filled = conf_templ % (ser,ser_hlr,ser_g1,ser_g2,n_tile,n_tile,n_pix,n_pix,filepath_bfit)
filename_conf = 'real%02d.bfit%02d.yaml' % (igal,iser)
filepath_conf = os.path.join(dirname_yaml, filename_conf)
file_conf = open(filepath_conf,'w')
file_conf.write(conf_filled)
file_conf.close()
filename_cmd = 'cmd.sh'
file_cmd = open(filename_cmd,'w')
file_cmd.write('python %s %s\n' % (filepath_galsim_yaml, filepath_conf))
file_cmd.close()
if (not args.reimage) and os.path.isfile(filepath_bfit):
logging.info('NOT creating %s' % filepath_bfit)
else:
subprocess.call(('sh',filename_cmd))
# create nosiy versions
noise_std = linalg.norm(image_stamp)/config['snr']
img_real = pyfits.getdata(filepath_real)
img_bfit = pyfits.getdata(filepath_bfit)
for nn in range(config['n_reps_diff']):
# add different noise maps
filename_noisy_bfit_diff = filename_bfit + ('.d%02d' % nn)
filepath_noisy_bfit_diff = os.path.join(dirname_images,filename_noisy_bfit_diff)
noise = random.randn(img_bfit.shape[0],img_bfit.shape[1])*noise_std
img_bfit_noisy_diff = img_bfit + noise
if (not args.reimage) and os.path.isfile(filepath_noisy_bfit_diff):
logging.info('NOT creating noisy images %s' % (filepath_noisy_bfit_diff))
else:
pyfits.writeto(filepath_noisy_bfit_diff,img_bfit_noisy_diff.astype(float32),clobber=True)
file_runlist.write('%s\t%s\n' % (filename_noisy_bfit_diff,filename_ini))
for nn in range(config['n_reps_same']):
# add same noise maps
filename_noisy_bfit_same = filename_bfit + ('.s%02d' % nn)
filepath_noisy_bfit_same = os.path.join(dirname_images,filename_noisy_bfit_same)
img_bfit_noisy_same = img_bfit + noise_same[nn]
if (not args.reimage) and os.path.isfile(filepath_noisy_bfit_same):
logging.info('NOT creating noisy image %s' % (filepath_noisy_bfit_same))
else:
pyfits.writeto(filepath_noisy_bfit_same,img_bfit_noisy_same.astype(float32),clobber=True)
file_runlist.write('%s\t%s\n' % (filename_noisy_bfit_same,filename_ini))
def generate_latent_points(latent_dim, n_samples):
# generate points in the latent space
x_input = random.randn(latent_dim * n_samples)
# reshape into a batch of inputs for the network
x_input = x_input.reshape(n_samples, latent_dim)
return x_input
import random
import sys
import os
import pandas as pd
from pylab import *
from scipy.cluster.vq import *
from numpy import *
from PIL import *
# generate test data
# generate 100 rows and 2 columns
class1 = 1.5 * random.randn(100, 2)
print(class1)
class2 = random.randn(100, 2) + array([5, 5])
# print(class2)
features = vstack((class1, class2))
#print(features[0:1])
#print("wanttttt")
# K-Means clustering
centroids, variance = kmeans(features, 2)
# print(centroids)
#print("varancccc")
# print(variance)
code, distance = vq(features, centroids)
print(vq(features, centroids))
# print(distance)
figure()
ndx = where(code == 0)[0]
