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')