def show_disc(newx, x): # for showing converted disc
global c
temp = []
for i in newx:
temp.append(i[0])
newx = temp
c += 1
maximum = max(max(x), max(newx))
minimum = min(min(x), min(newx))
conversion_label = str(c) + " Conversion"
#generating random colors for every conversion
new_ellipse = Ellipse(
xy=(newx[0], newx[1]),
width=2 * newx[2],
height=2 * newx[3],
color=[round(uni(0, 1), 1),
round(uni(0, 1), 1),
round(uni(0, 1), 1)],
label=conversion_label)
ax.add_patch(new_ellipse)
ax.set_xlim((minimum - maximum) * 2, (maximum + x[2]) * 2)
ax.set_ylim((minimum - maximum) * 2, (maximum + x[2]) * 2)
ax.legend() #for showing labels on graph
plt.pause(0.0001)
def show_polygon(newx): # for showing converted polygon
global c
temp = []
newy = []
for i in newx:
newy.append(i[1][0])
temp.append(i[0][0])
newx = temp
maximum = max(max(newx), max(newy))
minimum = min(min(newx), min(newy))
if minimum > 0:
minimum = 0
plt.xlim(minimum - maximum, maximum * 2)
plt.ylim(minimum - maximum, maximum * 2)
c += 1
conversion_label = str(c) + " Conversion"
plt.plot(
newx,
newy,
color=[round(uni(0, 1), 1),
round(uni(0, 1), 1),
round(uni(0, 1), 1)],
label=conversion_label)
plt.legend()
plt.pause(0.0001)
def ballcollide(self):
global balls
for w in balls:
if w != self:
xdist = self.x - w.x
ydist = self.y - w.y
realdist = sqrt(xdist**2 + ydist**2)
if realdist < self.r + w.r:
self.xvel = uni(-tem, tem)
self.yvel = uni(-tem, tem)
def benchmark():
"""
...
PARAMS: ...
RETURN: ...
"""
from random import uniform as uni
particles = [
Particle(uni(-1.0, 1.0), uni(-1.0, 1.0), uni(-1.0, 1.0))
for _ in range(1000)
]
simulator = ParticleSimulator(particles)
simulator.evolve(0.1)
def act(self, actionParams=None): """ Moves robot/particle in direction denoted by self.angle by STEP_SIZE. If there is a wall on the way, robot stays where he is and sets self.lastPointFree to False, otherwise sets it to True. It returns (dx, dy) where he wanted to move, no matter did he actually go there or not. actionParams parameter is not currently used. """ dx = self.STEP_SIZE * np.sin(self.angle) dy = self.STEP_SIZE * np.cos(self.angle) x, y = (self.p[0] + dx, self.p[1] + dy) deltaAngle = 0 if self.maze.isFree((x, y)): self.p = x, y self.lastPointFree = True else: # change direction so that robot don't stick in the wall if not actionParams is None: deltaAngle = actionParams self.angle += deltaAngle else: newAngle = uni(0, 2*np.pi) deltaAngle = newAngle - self.angle self.angle += deltaAngle self.lastPointFree = False if oneDistanceOnly: self.distFromBeacon = self.maze.distFromBeacon(self.p) else: self.distsFromBeacons = self.maze.distsFromBeacons(self.p) return deltaAngle
def pick(self): try: idx = bisect.bisect_left(self.distribution, uni(0, self.totalWeight)) return self.items[idx] except IndexError: # No items in distribution return None
def __init__(self, coords: tuple, count: int = 10, eps: float = 1):
"""
Coords -> ((x,y),(x,y))
Count -> default = 10
eps -> default = 1"""
if len(coords) > 2:
raise ("So much points")
self.eps = eps
self.coords = coords
self.count = count
self.points1 = [(self.coords[0][0] + uni(-self.eps, self.eps),
self.coords[0][1] + uni(-self.eps, self.eps))
for i in range(self.count)]
self.points2 = [(self.coords[1][0] + uni(-self.eps, self.eps),
self.coords[1][1] + uni(-self.eps, self.eps))
for i in range(self.count)]
def Rand(boolean):
if boolean == True:
#seconds = rnd(427, 604)
seconds = round(np.random.normal(loc=60 * 15 + 9, scale=64))
else:
seconds = round(uni(0.4, 3.1), 2)
return seconds
def run(self):
while True:
global mut, maxIn, qin, qout, h, href, R0, R1, H
mut.acquire()
qout = uni(0.5, 1) * (h[-1]**(1 / 2))
#print("qout - ", qout)
h.append(self.RungeKuttaSimples())
mut.release()
time.sleep(0.05)
#print("entrou")
self._stop()
def atualiza(self):
upas = sorted(self.upas, key = lambda x: x.fit)
temp = []
temp2 = []
mut = 0.75
for cUPA1 in upas:
for cUPA2 in upas:
if uni(0,1) > 1:
temp.append(cUPA1.mutacao(self.c))
temp.append(self.transa(cUPA1, cUPA2))
temp.append(cUPA1)
temp = sorted(temp, key = lambda y: y.fit)
for i in range(len(upas) - 1):
temp2.append(temp[i])
self.upas = temp2
def simulation_output(kpd,Apd,pgrid,transit,nsim,kindex,Aindex,sindex):
# we start with capital of index kindex
# with foreign assets of index Aindex
# with initial state of index sindex
# we then simulate the model over nsim periods
from random import uniform as uni
klin1 = np.linspace(pgrid['kmin'],pgrid['kmax'],pgrid['nk'])
Alin1 = np.linspace(pgrid['Amin'],pgrid['Amax'],pgrid['nA'])
ssim = np.zeros( nsim )
ksim = np.zeros( nsim + 1 )
Asim = np.zeros( nsim + 1 )
ssim[0] = sindex
ksim[0] = klin1[kindex]
Asim[0] = Alin1[Aindex]
# first decision
ksim[1] = kpdecided[sindex,Aindex,kindex]
Asim[1] = Apdecided[sindex,Aindex,kindex]
for i in xrange(1,nsim):
draw = uni(0,1)
kindex1 = np.where( klin1 == ksim[i] )[0][0]
Aindex1 = np.where( Alin1 == Asim[i] )[0][0]
if draw <= stoch_transit[ssim[i-1],0]:
ssim[i] = 0
elif (draw > stoch_transit[ssim[i-1],0]) and (draw<= stoch_transit[ssim[i-1],0]+stoch_transit[ssim[i-1],1]):
ssim[i] = 1
elif (draw > stoch_transit[ssim[i-1],0]+stoch_transit[ssim[i-1],1]) and (draw<= stoch_transit[ssim[i-1],0]+stoch_transit[ssim[i-1],1]+stoch_transit[ssim[i-1],2]):
ssim[i] = 2
else:
ssim[i] = 3
ksim[i+1] = kpdecided[ssim[i],Aindex1,kindex1]
Asim[i+1] = Apdecided[ssim[i],Aindex1,kindex1]
return np.array([ksim,Asim,ssim])
def get_cordinates():
return (uni(0, 1), uni(0, 1))
def generate_periodic(periodic_value):
""" takes a set periodic time and returns a randomly uniformly distributed value +/- 20%"""
half_range = periodic_value / 5
random_uniform_time = uni(periodic_value - half_range, periodic_value + half_range)
return random_uniform_time
def Wind_Oscillation(m):
tmp = (time.time() + uni(0, 2)) % 5
if tmp >= 2 and abs(min(rx0) - m.rx) > 10:
return tmp
else:
return 0
choice([3,7,234,43])
Out[14]: 234
## Можно экспортировать все функции из модуля, но лучше этого не делать
from random import *
gauss(0,1)
Out[16]: -1.063993696883884
## Можно двать модулям клички (alias)
from random import uniform as uni
uni(0,1)
Out[18]: 0.8462290298994931
import numpy as np
np.arccos(0.5)
Out[20]: 1.0471975511965979
## Модули могут иметь подмодули. Если модуль импортирован, то подмодуль
# не импортируется автоматически.
from os.path import abspath
abspath('..')
Out[22]: 'C:\\Users\\Алексей'
if w != self:
xdist = self.x - w.x
ydist = self.y - w.y
realdist = sqrt(xdist**2 + ydist**2)
if realdist < self.r + w.r:
self.xvel = uni(-tem, tem)
self.yvel = uni(-tem, tem)
balls = []
j = 0
while j != 120:
tem += 0.05
j += 1
radius = uni(2, 10)
x = uni(radius, width - radius)
y = uni(radius, height - radius)
nope = False
for w in balls:
xdist = x - w.x
ydist = y - w.y
if xdist**2 + ydist**2 < (radius + w.r)**2:
j -= 1
nope = True
break
if nope == False:
balls.append(ball(x, y, uni(-1, 1), uni(-1, 1), r=radius))
print(j)
while True:
def rndParams(): x, y = maze.randomFreePlace() angle = uni(0, 2*np.pi) return [x, y, angle]
import sys
from random import uniform as uni
# k = 50 & n = 1000000 for the contest
k = int(sys.argv[1])
n = int(sys.argv[2])
print(k)
print(n)
for i in range(k+n):
print("%f %f %f" %(uni(-100,100), uni(-100,100), uni(-100,100)))
def noiser(params): return [x + uni(-noiseLevel, noiseLevel) for x in params]
def parabolic_select_helper(fun, a, b, n):
for _ in range(0, n):
x = sorted([uni(a, b) for _ in range(0, 3)])
if fun(x[0]) >= fun(x[1]) <= fun(x[2]):
return x
raise Exception("Most likely your function is not unimodal")
def noiser(params): x = params[0] + uni(-nlimit, nlimit) y = params[1] + uni(-nlimit, nlimit) angle = params[2] + uni(-np.pi/100, np.pi/100) return [x, y, angle]
for i in range(qual):
summ += exp(sqrt(xr[i])) / (2 * sqrt(xr[i]))
print('inegral 1 linear = ', summ / qual)
summ = 0
for i in range(qual):
summ += (exp(xr[i]) + exp(1 - xr[i])) / 2
print('inegral 1 sim = ', summ / qual)
print('=====================================================================')
# counting 2nd integral =========================================== 2222222
N = 0
n = 0
xr = [uni(0, pi / 2) for i in range(qual)]
yr = [uni(0, pi / 2) for i in range(qual)]
norma = pi / 2
for i in range(qual):
if yr[i] < sin(xr[i]):
N += 1
n += 1
else:
N += 1
print('inegral 2 geometric = ', norma * norma * n / N)
summ = 0
c = 2 / pi
for i in range(qual):
summ += sin(xr[i]) / c
def gen(xy, e, c):
return [(xy[0] + uni(-e, e), xy[1] + uni(-e, e)) for i in range(c)]
from matplotlib.widgets import TextBox
import random
import math
initial_text = "0,0"
def distance(a: tuple, b: tuple):
return math.sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)
if __name__ == '__main__':
coords = ((1, 1), (2, 2))
eps = 0.5
count = 100
points1 = [(coords[0][0] + uni(-eps, eps), coords[0][1] + uni(-eps, eps))
for i in range(count)]
points2 = [(coords[1][0] + uni(-eps, eps), coords[1][1] + uni(-eps, eps))
for i in range(count)]
p1 = random.choice(points1)
p2 = random.choice(points2)
plt.plot([x for x, y in points1 + points2],
[y for x, y in points1 + points2], "ob")
plt.plot([p1[0], p2[0]], [p1[1], p2[1]], 'og')
for i in points1 + points2:
to = p1 if distance(i, p1) < distance(i, p2) else p2
plt.annotate('',
xy=to,
ax = fig.add_subplot(111, projection='3d') # input x1 x1 = [0, 0, 1, 1, 1, 4, 0, 4] # input x2 x2 = [0, 1, 0, 1, 1, 0, 4, 4] # input x3 x3 = [0, 1, 1, 1, 0, 4, 4, 4] # expected output y = [1, 1, 1, 1, 1, 0, 0, 0] # Generating random weights w1 = uni(-2.0, 3) w2 = uni(-2.0, 3) w3 = uni(-2.0, 3) # Fixing bias to 1 w = 1 #Learning rate lr = 0.01 output = [] flag = False count = 1 k = np.linspace(-6, 6, 5) m = np.linspace(-6, 6, num=5)
def randomParams(): return [uni(0, sqwid), uni(0,sqhei)]
if sys.argv[1] != "random":
for i in range(nb_body):
attributes = f.readline().split()
position = Vector2(float(attributes[0]), float(attributes[1]))
velocity = speed_scale * Vector2(float(attributes[2]),
float(attributes[3]))
mass = float(attributes[4])
color = (int(attributes[5]), int(attributes[6]),
int(attributes[7]))
real_radius = float(attributes[8])
draw_radius = float(attributes[9])
b = Body(position, velocity, mass, color, real_radius, draw_radius)
world.add(b)
else:
for i in range(nb_body):
position = Vector2(uni(-200, 200), uni(-200, 200))
velocity = Vector2(uni(-5, 5), uni(-5, 5))
mass = uni(1, 50)
color = (random.randrange(255), random.randrange(255),
random.randrange(255))
real_radius = random.randrange(10)
draw_radius = 2 * real_radius
b = Body(position, velocity, mass, color, real_radius, draw_radius)
world.add(b)
f.close()
#Permet de contrôler l'affichage des traçantes
should_erase_background = True
#simulator = Simulator(world, DummyEngine, DummySolver)
def randomPlace(self): return uni(0, self.totalWidth()), uni(0, self.totalHeight())
for epoch in range(epochs):
if (lookup):
break
if (training_mode):
noise1Array = []
noise2Array = []
discriminatorLossesArray = []
generatorLossesArray = []
seedsArray = []
for batch in range(len(training_loader)):
seedsArray.append(uni(0.0, 1.0))
noise = noiseGenerator.generate(batchSize, channels,
(noiseHeight, noiseWidth))
noise1Array.append(noise)
noise = noiseGenerator.generate(batchSize, channels,
(noiseHeight, noiseWidth))
noise2Array.append(noise)
discriminatorIterationLosses = []
for imageIndex, image in enumerate(training_loader, 0):
if (imageIndex % tempDiscriminatorRatio == 0):
random = uni(0.00, 1.00)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Generate and print float matrix of specified dimensions
# (first two arguments). The elements are separated by '\t'.
#
# Example usage:
# ./random_matrix.py 4 5
import sys
from random import uniform as uni
for _ in range(0, int(sys.argv[1])):
sys.stdout.write(
'\t'.join([str(uni(0, 99))
for x in range(0, int(sys.argv[2]))]) + '\n')