def expon_cdf_pdf():
x = np.linspace(0, 5, 100)##return evenly spaced samples, calculated over the interval [0,5]
rv=E(scale = 1)#the scale is 1
plt.plot(x, rv.pdf(x), color='blue')#make pdf chart
plt.plot(x, rv.cdf(x), color='red') #make cdf chart
plt.savefig('fig.png')
def law_of_large_numbers():
x = np.arange(1, 1001, 1)
r = bernoulli.rvs(0.3, size=1000)
y = []
rsum =0.0
for i in range(1000):
if r[i]==1:
rsum=rsum+1
y.append(rsum/(i+1))
plt.plot(x, y, color='red')
plt.savefig('law_of_large_numbers.png')
def law_of_large_numbers():
x = np.arange(1, 1001, 1)
r1 = binom.rvs(10, 0.6, size=1000)
r2 = poisson.rvs(mu=6, size=1000)
r3 = norm.rvs(loc=6, size=1000)
y = []
rsum=0.0
for i in range(1000):
rsum=rsum+(r1[i]+r2[i]+r3[i])
y.append(rsum/((i+1)*3)-6)
plt.plot(x, y, color='red')
plt.savefig('law_of_large_numbers.png')
def linregress1():
x = np.linspace(-5, 5, num=150)
y = x + np.random.normal(size=x.size)
y[12:14] += 10
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
log(slope)
log(intercept)
log(r_value)
log(p_value)
log(std_err)
plt.plot(x, y, 'b.')
plt.plot(x, slope * x + intercept, 'r-')
plt.savefig('linregress1.png')
def norm_pdf():
x = np.linspace(-10, 10, 100)#return evenly spaced samples, calculated over the interval [-10,10]
rv1=N(loc=0, scale = 1)#the mean is 0, the standard deviation is 1
rv2=N(loc=-5, scale = 1)#the mean is -5, the standard deviation is 1
rv3=N(loc=0, scale = 3)#the mean is 0, the standard deviation is 3
plt.plot(x, rv1.pdf(x), color='green')#make chart
plt.plot(x, rv2.pdf(x), color='blue')#make chart
plt.plot(x, rv3.pdf(x), color='red')#make chart
plt.savefig('fig.png')
def expon_pdf():
x = np.linspace(0, 20, 100)#return evenly spaced samples, calculated over the interval [0,20]
rv1=E(scale = 1.5)#the scale is 1.5
rv2=E(scale = 1.0)#the scale is 1.0
rv3=E(scale = 0.5)#the scale is 0.5
plt.plot(x, rv1.pdf(x), color='green')#make chart
plt.plot(x, rv2.pdf(x), color='blue')#make chart
plt.plot(x, rv3.pdf(x), color='red')#make chart
plt.savefig('fig.png')