def perform_draw(names, num_winners=1, num_iterations=32):
for i in range(1,num_iterations):
clear_screen()
delay = e(i/float(num_iterations))-1.5
if delay < 0: delay = 0.1
candidates = random.sample(names, num_winners)
print_message(", ".join(candidates))
sleep(delay)
clear_screen()
sleep(2.5)
winners = random.sample(names, num_winners)
if num_winners > 1: title = "Vinnerne"
else: title = "Vinneren"
message = title + " er: " + ", ".join(winners) + ". Gratulerer!"
print_message(message, spaces=30)
raw_input()
def node_output(inputs, weights):
# weight[0] is bias weight
activation = weights[0]
for i in range(len(inputs)):
activation += inputs[i] * weights[i + 1]
try:
# compute the simoid(sigmoid?) of the activation
output = 1 / (1 + e(-1 * activation))
except OverflowError:
# catch cases where e^-a overflows
if activation < 0:
output = 0
else:
output = 1
return output
def score_plan(plan, const):
""" Accepts a districting plan in the form of a networkx graph and scores
it using two sub-scores: population balance and district contiguity. The
score function takes the form of an energy function: e^-x. A parameterised
constant is used to appropriately scale the results of the sub-scores """
# Get population score parameter
pop_param = score_pop(plan)
# Get contiguity score parameter
contig_param = score_contig(plan)
# Energy function with constant to adjust 'strength' of scoring
score = e(-const * pop_param * contig_param)
return score
def simulated_annealing():
current = INITIAL_POINT
t = 0
while True:
T = schedule(t)
x, y = current
if T == 0:
print(F(current), t)
return current
angle = random.uniform(-180, 180) * math.pi / 180
next = (x + RADIUS * math.cos(angle), y + RADIUS * math.sin(angle))
dE = F(next) - F(current)
if dE > 0:
current = next
else:
p = e(dE / T)
current = next if random.random() < p else current
t = t + 1
def update_variance_winner(i, j, k):
inner_first_para_upper = worker_quality[k][
0] * worker_quality[k][1] * e(score[i]) * e(score[j])
inner_first_para_lower = pow(
(worker_quality[k][0] * e(score[i]) + worker_quality[k][1] * e(score[j])), 2)
inner_second_para = e(
score[i]) * e(score[j]) / pow(e(score[i]) + e(score[j]), 2)
max_candidate_one = 1 + \
pow(variance[i], 2) * (inner_first_para_upper /
inner_first_para_lower - inner_second_para)
max_candidate_two = 0.0001
result = pow(variance[i], 2) * max(max_candidate_one, max_candidate_two)
return result
def simulatedAnnealing(M, N, D, W, start, end):
# Takes Tmax and dT in to allow for experimentation.
# When everything's done it'll only take the size of a the board, and eggs (M, N, k)
State = Switchboard(M, N, D, W) # The State to be returned when optimal
Temp = 0.07
dT = 0.00001
targetBoardEvaluation = 0.7
while (State.evaluateBoard() < targetBoardEvaluation):
neighbours = State.generateNeighbours()
newState = None # The best neighbour
for neighbour in neighbours: # Loop through neighbours to find the best one
if (neighbour.evaluateBoard() > newState):
newState = neighbour
q = ((newState.evaluateBoard()-State.evaluateBoard())/State.evaluateBoard())
p = math.min(1, math.e((-q)/Temp))
x = random.random() # Random number between 0 and 1
if (x > p ):
State = newState
else:
State = neighbours[random.randint(0, len(neighbours))] # None of them were very good, choose a random one
Temp -= dT
return State
return ret
ret = 0
for i in range(len(points)):
print("{0}*".format(points[i][1]), end="")
ret += points[i][1] * L(i)
if i + 1 < len(points):
print(" + ", end="")
if type(x) == float:
print(" = {0}".format(ret))
return ret
return ""
print(
largrandian_interpolation(((1.2, 1.5095), (1.3, 1.6984), (1.4, 1.9043),
(1.5, 2.1293), (1.6, 2.3756)), 1.1))
x = 1.1
print((e(x) - e(-x)) / 2)
print()
print(
largrandian_interpolation(((1.2, 1.5095), (1.3, 1.6984), (1.4, 1.9043),
(1.5, 2.1293), (1.6, 2.3756)), 1.3))
x = 1.3
print((e(x) - e(-x)) / 2)
print()
print(
largrandian_interpolation(((1.2, 1.5095), (1.3, 1.6984), (1.4, 1.9043),
(1.5, 2.1293), (1.6, 2.3756)), 1.6))
x = 1.6
print((e(x) - e(-x)) / 2)
R = [[h(1) * (f(a) + f(b))]]
i, j = 1, 0
while max_steps > 0:
if j == 0:
hi = h(i)
R.append([0.5 * R[i - 1][0] + hi * (sum_of_f(hi, i))])
j += 1
while (j <= i):
R[i].append(((1 / (pow(4, j) - 1)) *
(pow(4, j) * R[i][j - 1] - R[i - 1][j - 1])))
j += 1
if R[i][j - 1] == R[i][j - 2]:
print(
str(R).replace('],',
'\n').replace('[[',
' ').replace('[',
'').replace(']', ''))
return R[i][j - 1]
j = 0
i += 1
max_steps -= 1
print(
str(R).replace('],', '\n').replace('[[',
' ').replace('[',
'').replace(']', ''))
return R[i - 1][j - 1]
print(romberg(lambda x: x * e(-x), 0, 2, 10))
def rejection():
c, Y, U = 2/e(1), exponential(1/2), random()
while U >= c * Y * e(- Y / 2):
Y, U = exponential(1/2), random()
return(Y)
def sigmoid(x, sigma=SIGMA): return 1/(1 + e(-sigma*x))
def compute_function(cls, x: float) -> float:
return 1 / (1 + e(-x))
def get_c_value(self, S, d, t, d1, K, r, d2):
return S * e(-d * t) * norm.cdf(d1) - K * e(-r * t) * norm.cdf(d2)
60943702770539217176293176752384674818467669405132
00056812714526356082778577134275778960917363717872
14684409012249534301465495853710507922796892589235
42019956112129021960864034418159813629774771309960
51870721134999999837297804995105973173281609631859
50244594553469083026425223082533446850352619311881
71010003137838752886587533208381420617177669147303
59825349042875546873115956286388235378759375195778
18577805321712268066130019278766111959092164201989)
if (UserIPAddress == math.isclose(to your house)) proceed with {
function(ddos(userip(getip(sendrequest(retreieverequest(parserequest(dumprequest(ddosrequest(((UserIPAddress)))))))))))
}
#get mathemeaticals constant
sum1=sum1+(1/math.factorial(i)) * math.e(12^UserIPAddress / sum)
#send request to mars rover
if @bot.command() rover == function->GetMarsRoverName { }
send::ping + request::to mars.rover 1
ask mars.rover 1 for image:
return nasa.api.image
ip address coordinates: aarray([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
def arps_exp_rate(qi, di, b):
return qi*math.e(-di)
# EXPRESSION FILE
###########################################################
# The idea is to create a toy dataset including the two types of termination
# we can see in the RNA seq profiles to test two different things:
# 1. The size of the windows required to detect the signals
# 2. The whole approach
max_exp = 200
genome_size = 9000
# We need a decay list of numbers:
decay = []
c = 1/e(2)
x = max_exp
while x >= 0:
decay.append(x)
c = c*1.05
x = x-c
# Arrive to 0
decay = decay[1:]+[0]
# We will put the information in the toyset file
fo = open('./datasets/toyset.txt','w')
# Define the properties of the population and the position for 2 types of
def f(x): return 100*e(-0.0482*x)
#!/usr/bin/python3
from math import exp as e
print(e(1))
# print(help('modules')) # 当前的第三方库，打印要很久
def get_p_value(self, S, d, t, d1, K, r, d2):
return K * e(-r * t) * norm.cdf(-d2) - S * e(-d * t) * norm.cdf(-d1)
print(math.pi) # 内置的圆周率常数
print(' ')
# 使用别名导入math库进行数学运算
print('使用别名导入math库进行数学运算')
print(m.sin(1)) # 计算正弦
print(m.cos(1)) # 计算余弦
print(m.tan(1)) # 计算正切
# print(m.cot(0)) # 计算余切
print(m.pi) # 内置的圆周率常数
print(' ')
# 通过名称导入指定函数
from math import exp as e # 只导入math库中的exp函数，并起别名e
print('通过名称导入指定函数')
print(e(1)) # 计算指数
# print(sin(1)) # 此时sin(1)和math.sin(1)都会出错，因为没被导入
print(' ')
# 导入库中所有函数
# 直接导入math库，也就是去掉math.，但如果大量地这样引入第三库，就容易引起命名冲突
# from math import *
# print('导入库中所有函数')
# print(sin(1))
# print(exp(1))
# print(' ')
# 导入future特征
# 将print变成函数形式，即用print(a)格式输出
# from __future__ import print_function
def get_c_delta(self, d, t, d1):
return e(-d * t) * norm.cdf(d1)
def sigmoid_prime(x, sigma=SIGMA): return (sigma*e(sigma*x))/((e(sigma*x) + 1)**2)
def get_p_delta(self, d, t, d1):
return e(-d * t) * (norm.cdf(d1) - 1)
def F(point):
x, y = point
return 4 * e(-(x**2 + y**2)) + e(-((x - 5)**2 + (y - 5)**2)) + e(-(
(x + 5)**2 + (y - 5)**2)) + e(-((x - 5)**2 +
(y + 5)**2)) + e(-((x + 5)**2 +
(y + 5)**2))
def get_gamma(self, d, d1, S, iv, t):
return e(-d * t) * norm().pdf(d1) / (S * iv * sqrt(t))
def schedule(t):
return T0 * e(-t / 1000)
def get_vega(self, S, d, t, d1):
return 0.01 * S * e(-d * t) * sqrt(t) * norm.pdf(-d1)
def arps_exponential_rate(qi, di, b):
return qi * math.e(-di)
def get_p_theta(self, S, iv, d, d1, t, r, K, d2, days):
return (1 / days) * (-(S * iv * e(-d * t) * norm.pdf(-d1) /
(2 * sqrt(t))) +
(r * K * e(-r * t) * norm.cdf(-d2)) -
(d * S * e(-d * t) * norm.cdf(-d1)))
#import math math.exp(i)
from math import exp as e
def fac(x):
if x <= 1:
return 1
else:
val = fac(x-1)
val *= x #val = val * x
return val
# def fac(x):
#return 1 if x <= 1 else x*fac(x-1) #Equivalente a operadores ternaros en Java
def serie(x):
limit = 21
acu = 1 + x
for i in range(2,limit):
acu += x**i / fac(i)
return acu
if __name__ == '__main__':
for i in range(1,11):
valor_a = serie(i)
valor_r = e(i)
e_v = valor_r - valor_a
print("Valor real: " + str(valor_r) + "\nValor aprox: " + str(valor_a) + "\nError verdadero: " + str(e_v))
import matplotlib.pyplot as plt
import numpy as np
import math
from math import exp as e
from math import pi as pi
x = np.linspace(-3, 3, 5000)
y = []
xo = np.linspace(0, 1, 25)
yo = [e(i) - 1 for i in xo]
x1 = [1, 3]
y1 = [e(1) - 1, e(1) - 1]
for t in x:
ima = 1.3867495867890338
for n in range(1, 101):
wolfram = (2 / 3) * math.cos(n * t * (2 * pi / 3)) * (
(e(1) * n * (2 * pi / 3) * math.sin(n * (2 * pi / 3)) +
e(1) * math.cos(n * (2 * pi / 3)) - 1) /
(n * n * (2 * pi / 3) *
(2 * pi / 3) + 1) - math.sin(n * (2 * pi / 3)) / (n *
(2 * pi / 3)) +
((e(1) - 1) *
(math.sin(3 * n * (2 * pi / 3)) - math.sin(n * (2 * pi / 3)))) /
(n * (2 * pi / 3))) + (2 / 3) * math.sin(n * t * (2 * pi / 3)) * (
((1 - (e(1) - 1) * n * n * (2 * pi / 3) *
(2 * pi / 3)) * math.cos(n * (2 * pi / 3)) + e(1) * n *
(2 * pi / 3) * math.sin(n * (2 * pi / 3)) - 1) /
(n * n * n * (2 * pi / 3) * (2 * pi / 3) * (2 * pi / 3) + n *
(2 * pi / 3)) + (2 * (e(1) - 1) * math.sin(n * (2 * pi / 3)) *
def normal_dist(x,u,sigma): a = 1./sigma*sqrt(2*pi) b = -(u-x)**2 c = 2*sigma**2 return a * e(b/c)
def sigmoid(z):
return 1 / (1 + e(-z))
def function(theta, n):
for i in range(n):
mu = random()
theta += e(-(1. / mu - 1.) ** 2) / (mu ** 2)
theta /= n * 2.
print("Interaciones: {}, Resultado: {}".format(n, theta))
def sig(z):
return 1 / (1 + e(-z))
def f(x): fr = (1/(1+e(-x))) min = fr - 0.5 return 2*min
#-------------------------------------------------- # Python is very parsimonious in its use of memory. # Many basic mathematical functions -besides operators- have to be imported from modules import math math.e math.pi math.log(math.e) math.e(math.log(0)) math.e**math.log(7) must_be_true = math.e**math.log(7) == 7 must_be_true = (math.e**math.log(7) - 7) < 0.0000001 # When you import a module, you can use any object in the module by using the module name and dot before the object (usually a function or a constant). # You can choose your own abbreviations for modules