1 files changed, 0 insertions, 95 deletions
diff --git a/test.py b/test.py
deleted file mode 100644
index d1db16d..0000000
--- a/test.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-
-A = np.random.uniform(0.1, 0.9)
-B = np.random.uniform(0.1, 0.9)
-
-def q_xt_xt_1_simple(x, t):
-    mean = A * x
-    std = B
-    return np.random.normal(mean, std)
-
-def q_xt_x0_simple_damien(x, t):
-    mean = A ** t * x
-    std = np.sqrt(sum([A ** (2*i) for i in range(t)])) * B
-    return np.random.normal(mean, std)
-
-
-def q_xt_xt_1(x, t):
-    alpha = ALPHA[t]
-    mean = np.sqrt(alpha) * x
-    std = 1 - alpha
-    return np.random.normal(mean, std)
-
-def q_xt_x0_paper(x, t):
-    alpha_bar = ALPHA_BAR[t]
-    mean = np.sqrt(alpha_bar) * x
-    std = 1 - alpha_bar
-    return np.random.normal(mean, std)
-
-def q_xt_x0_damien(x, t):
-    alpha_bar = ALPHA_BAR[t]
-    cum_sq_sum = sum([np.prod(ALPHA[s+2:t+1]) * (1 - ALPHA[s+1])**2 for s in range(t)])
-    mean = np.sqrt(alpha_bar) * x
-    std = np.sqrt(cum_sq_sum)
-    return np.random.normal(mean, std)
-
-T = 100
-BETA = np.concatenate(([0], np.linspace(1e-4, 2e-2, T)))
-ALPHA = 1 - BETA
-ALPHA_BAR = np.cumprod(ALPHA)
-
-N = int(1e6)
-x0 = 1
-
-xs_implicit = np.array([x0] * N)
-for t in range(1, T+1):
-    xs_implicit = q_xt_xt_1(xs_implicit, t)
-
-xs_explicit_paper = q_xt_x0_paper(np.array([x0] * N), T)
-xs_explicit_damien = q_xt_x0_damien(np.array([x0] * N), T)
-
-plt.figure()
-bins = np.linspace(min(
-                xs_implicit.min(),
-                xs_explicit_paper.min(),
-                xs_explicit_damien.min(),
-            ), max(
-                xs_implicit.max(),
-                xs_explicit_paper.max(),
-                xs_explicit_damien.max(),
-            ), 100)
-plt.hist(xs_implicit, bins=bins, alpha=0.5, label="q_xt_xt_1")
-plt.hist(xs_explicit_paper, bins=bins, alpha=0.5, label="q_xt_x0_paper")
-plt.hist(xs_explicit_damien, bins=bins, alpha=0.5, label="q_xt_x0_damien")
-plt.legend()
-plt.savefig("q_xt_xt_1_vs_q_xt_x0.tmp.png")
-plt.show()
-
-xs_implicit = np.array([x0] * N)
-for t in range(1, T+1):
-    xs_implicit = q_xt_xt_1_simple(xs_implicit, t)
-
-xs_explicit_damien = q_xt_x0_simple_damien(np.array([x0] * N), T)
-
-plt.figure()
-bins = np.linspace(min(xs_implicit.min(), xs_explicit_damien.min()), max(xs_implicit.max(), xs_explicit_damien.max()), 100)
-plt.hist(xs_implicit, bins=bins, alpha=0.5, label="q_xt_xt_1_simple")
-plt.hist(xs_explicit_damien, bins=bins, alpha=0.5, label="q_xt_x0_simple_damien")
-plt.legend()
-plt.savefig("q_xt_xt_1_simple_vs_q_xt_x0_simple.tmp.png")
-plt.show()
-
-A1, B1, A2, B2, A3, B3 = np.random.uniform(0, 1, 6)
-x1 = np.random.normal(A1, B1, N)
-x2 = np.random.normal(A2 * x1, B2, N)
-x3 = np.random.normal(A3 * x2, B3, N)
-x3_ = np.random.normal(A1 * A2 * A3, np.sqrt(A3**2 * A2**2 * B1**2 + A3**2 * B2**2 + B3**2), N)
-
-plt.figure()
-bins = np.linspace(min(x3.min(), x3_.min()), max(x3.max(), x3_.max()), 100)
-plt.hist(x3, bins=bins, alpha=0.5, label="normal")
-plt.hist(x3_, bins=bins, alpha=0.5, label="product")
-plt.legend()
-plt.savefig("product_normal.tmp.png")
-plt.show()