def test():
obj = mt.get_state()
mt.set_state(obj)
obj2 = mt.get_state()
if (obj2[1] - obj[1]).any():
raise SystemExit("Failed seed test.")
print("First random number is", random())
print("Average of 10000 random numbers is", np.sum(random(10000),axis=0)/10000.)
x = random([10,1000])
if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000:
raise SystemExit("random returned wrong shape")
x.shape = (10000,)
print("Average of 100 by 100 random numbers is", np.sum(x,axis=0)/10000.)
y = uniform(0.5,0.6, (1000,10))
if len(y.shape) !=2 or y.shape[0] != 1000 or y.shape[1] != 10:
raise SystemExit("uniform returned wrong shape")
y.shape = (10000,)
if np.minimum.reduce(y) <= 0.5 or np.maximum.reduce(y) >= 0.6:
raise SystemExit("uniform returned out of desired range")
print("randint(1, 10, shape=[50])")
print(randint(1, 10, shape=[50]))
print("permutation(10)", permutation(10))
print("randint(3,9)", randint(3,9))
print("random_integers(10, shape=[20])")
print(random_integers(10, shape=[20]))
s = 3.0
x = normal(2.0, s, [10, 1000])
if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000:
raise SystemExit("standard_normal returned wrong shape")
x.shape = (10000,)
mean_var_test(x, "normally distributed numbers with mean 2 and variance %f"%(s**2,), 2, s**2, 0)
x = exponential(3, 10000)
mean_var_test(x, "random numbers exponentially distributed with mean %f"%(s,), s, s**2, 2)
x = multivariate_normal(np.array([10,20]), np.array(([1,2],[2,4])))
print("\nA multivariate normal", x)
if x.shape != (2,): raise SystemExit("multivariate_normal returned wrong shape")
x = multivariate_normal(np.array([10,20]), np.array([[1,2],[2,4]]), [4,3])
print("A 4x3x2 array containing multivariate normals")
print(x)
if x.shape != (4,3,2): raise SystemExit("multivariate_normal returned wrong shape")
x = multivariate_normal(np.array([-100,0,100]), np.array([[3,2,1],[2,2,1],[1,1,1]]), 10000)
x_mean = np.sum(x,axis=0)/10000.
print("Average of 10000 multivariate normals with mean [-100,0,100]")
print(x_mean)
x_minus_mean = x - x_mean
print("Estimated covariance of 10000 multivariate normals with covariance [[3,2,1],[2,2,1],[1,1,1]]")
print(np.dot(np.transpose(x_minus_mean),x_minus_mean)/9999.)
x = beta(5.0, 10.0, 10000)
mean_var_test(x, "beta(5.,10.) random numbers", 0.333, 0.014)
x = gamma(.01, 2., 10000)
mean_var_test(x, "gamma(.01,2.) random numbers", 2*100, 2*100*100)
x = chi_square(11., 10000)
mean_var_test(x, "chi squared random numbers with 11 degrees of freedom", 11, 22, 2*np.sqrt(2./11.))
x = F(5., 10., 10000)
mean_var_test(x, "F random numbers with 5 and 10 degrees of freedom", 1.25, 1.35)
x = poisson(50., 10000)
mean_var_test(x, "poisson random numbers with mean 50", 50, 50, 0.14)
print("\nEach element is the result of 16 binomial trials with probability 0.5:")
print(binomial(16, 0.5, 16))
print("\nEach element is the result of 16 negative binomial trials with probability 0.5:")
print(negative_binomial(16, 0.5, [16,]))
print("\nEach row is the result of 16 multinomial trials with probabilities [0.1, 0.5, 0.1 0.3]:")
x = multinomial(16, [0.1, 0.5, 0.1], 8)
print(x)
print("Mean = ", np.sum(x,axis=0)/8.)
def test():
obj = mt.get_state()
mt.set_state(obj)
obj2 = mt.get_state()
if (obj2[1] - obj[1]).any():
raise SystemExit, "Failed seed test."
print "First random number is", random()
print "Average of 10000 random numbers is", np.sum(random(10000),
axis=0) / 10000.
x = random([10, 1000])
if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000:
raise SystemExit, "random returned wrong shape"
x.shape = (10000, )
print "Average of 100 by 100 random numbers is", np.sum(x, axis=0) / 10000.
y = uniform(0.5, 0.6, (1000, 10))
if len(y.shape) != 2 or y.shape[0] != 1000 or y.shape[1] != 10:
raise SystemExit, "uniform returned wrong shape"
y.shape = (10000, )
if np.minimum.reduce(y) <= 0.5 or np.maximum.reduce(y) >= 0.6:
raise SystemExit, "uniform returned out of desired range"
print "randint(1, 10, shape=[50])"
print randint(1, 10, shape=[50])
print "permutation(10)", permutation(10)
print "randint(3,9)", randint(3, 9)
print "random_integers(10, shape=[20])"
print random_integers(10, shape=[20])
s = 3.0
x = normal(2.0, s, [10, 1000])
if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000:
raise SystemExit, "standard_normal returned wrong shape"
x.shape = (10000, )
mean_var_test(
x,
"normally distributed numbers with mean 2 and variance %f" % (s**2, ),
2, s**2, 0)
x = exponential(3, 10000)
mean_var_test(
x, "random numbers exponentially distributed with mean %f" % (s, ), s,
s**2, 2)
x = multivariate_normal(np.array([10, 20]), np.array(([1, 2], [2, 4])))
print "\nA multivariate normal", x
if x.shape != (2, ):
raise SystemExit, "multivariate_normal returned wrong shape"
x = multivariate_normal(np.array([10, 20]), np.array([[1, 2], [2, 4]]),
[4, 3])
print "A 4x3x2 array containing multivariate normals"
print x
if x.shape != (4, 3, 2):
raise SystemExit, "multivariate_normal returned wrong shape"
x = multivariate_normal(np.array([-100, 0, 100]),
np.array([[3, 2, 1], [2, 2, 1], [1, 1, 1]]), 10000)
x_mean = np.sum(x, axis=0) / 10000.
print "Average of 10000 multivariate normals with mean [-100,0,100]"
print x_mean
x_minus_mean = x - x_mean
print "Estimated covariance of 10000 multivariate normals with covariance [[3,2,1],[2,2,1],[1,1,1]]"
print np.dot(np.transpose(x_minus_mean), x_minus_mean) / 9999.
x = beta(5.0, 10.0, 10000)
mean_var_test(x, "beta(5.,10.) random numbers", 0.333, 0.014)
x = gamma(.01, 2., 10000)
mean_var_test(x, "gamma(.01,2.) random numbers", 2 * 100, 2 * 100 * 100)
x = chi_square(11., 10000)
mean_var_test(x, "chi squared random numbers with 11 degrees of freedom",
11, 22, 2 * np.sqrt(2. / 11.))
x = F(5., 10., 10000)
mean_var_test(x, "F random numbers with 5 and 10 degrees of freedom", 1.25,
1.35)
x = poisson(50., 10000)
mean_var_test(x, "poisson random numbers with mean 50", 50, 50, 0.14)
print "\nEach element is the result of 16 binomial trials with probability 0.5:"
print binomial(16, 0.5, 16)
print "\nEach element is the result of 16 negative binomial trials with probability 0.5:"
print negative_binomial(16, 0.5, [
16,
])
print "\nEach row is the result of 16 multinomial trials with probabilities [0.1, 0.5, 0.1 0.3]:"
x = multinomial(16, [0.1, 0.5, 0.1], 8)
print x
print "Mean = ", np.sum(x, axis=0) / 8.
