def test_sim():
passed_count = 0
print("Test 1: Uniform distribution")
d = Distribution(unif_dist)
result = sp.zeros(10)
for i in range(10000):
out = d.sample()
result[out - 1] += 1
csr = sp.stats.chisquare(result, 1000 * sp.ones(10))
print("Observed frequencies:", result)
print("Expected frequencies:", 1000 * sp.ones(10))
print("Chi-square statistic:", csr.statistic)
print("p-value: ", csr.pvalue)
if csr.pvalue < 0.01:
print("Test FAILED")
else:
print("Test PASSED")
passed_count += 1
if plot_tests:
plt.figure()
plt.subplot(2, 1, 1)
plt.bar(x=1 + sp.arange(10),
height=result,
tick_label=1 + sp.arange(10))
plt.title("Uniform distribution: observed (top) vs. expected (bottom)")
plt.subplot(2, 1, 2)
plt.bar(x=1 + sp.arange(10),
height=0.1 * sp.ones(10),
tick_label=1 + sp.arange(10))
plt.savefig("uniform.png")
print("Test 2: Drawing from an urn")
result = sp.zeros(3)
picks = []
d = Distribution(rgb)
for i in range(10000):
picks.append(d.sample())
result[0] = sum([pick == "red" for pick in picks])
result[1] = sum([pick == "green" for pick in picks])
result[2] = sum([pick == "blue" for pick in picks])
csr = sp.stats.chisquare(result, (10000 / 18) * sp.array([3, 7, 8]))
print("Observed frequencies:", result)
print("Expected frequencies:", (10000 / 18) * sp.array([3, 7, 8]))
print("Chi-square statistic:", csr.statistic)
print("p-value: ", csr.pvalue)
if csr.pvalue < 0.01:
print("Test FAILED")
else:
print("Test PASSED")
passed_count += 1
if plot_tests:
plt.figure()
plt.subplot(2, 1, 1)
plt.bar(x=sp.arange(3),
height=result,
tick_label=["red", "green", "blue"])
plt.title("Uniform distribution: observed (top) vs. expected (bottom)")
plt.subplot(2, 1, 2)
plt.bar(x=sp.arange(3),
height=(1 / 18) * sp.array([3, 7, 8]),
tick_label=["red", "green", "blue"])
plt.savefig("urn.png")
print("Test 3: Benford's law")
result = sp.zeros(9)
d = Distribution(benford)
for i in range(10000):
out = d.sample()
result[out - 1] += 1
csr = sp.stats.chisquare(
result, 10000 * sp.array(
[0.301, 0.176, 0.125, 0.097, 0.079, 0.067, 0.058, 0.051, 0.046]))
print("Observed frequencies:", result)
print(
"Expected frequencies:", 10000 * sp.array(
[0.301, 0.176, 0.125, 0.097, 0.079, 0.067, 0.058, 0.051, 0.046]))
print("Chi-square statistic:", csr.statistic)
print("p-value: ", csr.pvalue)
if csr.pvalue < 0.01:
print("Test FAILED")
else:
print("Test PASSED")
passed_count += 1
if plot_tests:
plt.figure()
plt.subplot(2, 1, 1)
plt.bar(x=1 + sp.arange(9),
height=result / 10000,
tick_label=1 + sp.arange(9))
plt.title("Uniform distribution: observed (top) vs. expected (bottom)")
plt.subplot(2, 1, 2)
plt.bar(x=1 + sp.arange(9),
height=sp.array([
0.301, 0.176, 0.125, 0.097, 0.079, 0.067, 0.058, 0.051,
0.046
]),
tick_label=1 + sp.arange(9))
plt.savefig("benford.png")
'''
if simbonus:
print("Test 4: Drawing from an urn, with a non-normalized distribution")
result = sp.zeros(3)
picks = []
for i in range(10000):
picks.append(simfunc(rgb_unscaled))
result[0] = sum([pick == "red" for pick in picks])
result[1] = sum([pick == "green" for pick in picks])
result[2] = sum([pick == "blue" for pick in picks])
csr = sp.stats.chisquare(result, (10000/18) * sp.array([3,7,8]))
print("Observed frequencies:", result)
print("Expected frequencies:", (10000/18) * sp.array([3,7,8]))
print("Chi-square statistic:", csr.statistic)
print("p-value: ", csr.pvalue)
if csr.pvalue < 0.01:
print("Test FAILED")
else:
print("Test PASSED")
plt.figure()
plt.subplot(2, 1, 1)
plt.bar(x=sp.arange(3), height=result, tick_label = ["red", "green", "blue"])
plt.title("Uniform distribution: observed (top) vs. expected (bottom)")
plt.subplot(2, 1, 2)
plt.bar(x=sp.arange(3), height=(1/18)*sp.array([3,7,8]), tick_label = ["red", "green", "blue"])
plt.savefig("urnbonus.png")
'''
return passed_count
import torch
from distribution import Distribution
from visualize import *
n_epochs = 10000
lr = 0.05
batch_size = 64
vis_iter = 200
target = Distribution([.1, .3, .4, .1, .1], [-3, 3, 15, 10, -10],
[1, 2, 3, 1, 5])
Q = Distribution(K=10, requires_grad=True)
plot_dist(target, 'target', '#ff8200', (-20, 20))
plot_dist(Q, 'Q', '#7600fe', (-20, 20))
optimizer = torch.optim.Adam(Q.parameters(), lr=lr)
for epoch in range(int(n_epochs)):
x = target.sample(batch_size)
loss = torch.pow(Q(x) - target(x), 2).mean() # MSE
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch % vis_iter == vis_iter - 1:
plot_loss(epoch, loss)
plot_dist(Q, 'Q', '#7600fe', (-20, 20))
