feat(script): Optimize normal distribution generator

* feat(script): Finish experiment 1 and 2

Signed-off-by: KunoiSayami <[email protected]>

1 files changed, 18 insertions, 7 deletions

diff --git a/ex_normal_distribution.py b/ex_normal_distribution.py
index f82df93..272b9b6 100755
--- a/ex_normal_distribution.py
+++ b/ex_normal_distribution.py
@@ -1,24 +1,35 @@
 #!/usr/bin/env python3
+import numpy as np
 import matplotlib.pyplot as plt
+from matplotlib.ticker import PercentFormatter
 
 
 def function_pdf(element: list[int]):
-    plt.hist(element, bins=500)
+    plt.hist(element, bins=500, histtype='step')
     plt.show()
 
 
 def function_cdf(element: list[int]):
-    plt.hist(element, bins=500, cumulative=True)
+    plt.hist(element, bins=500, cumulative=True, histtype='step')
     plt.show()
 
 
-def normal_distribution():
+def function_1000_cdf(element: list[int]):
+    plt.hist(element, bins=250, histtype='step', label='x', cumulative=True,
+             weights=np.ones(len(element)) / len(element))
+    plt.hist(element[:1000], bins=100, histtype='step', label='y', cumulative=True, weights=np.ones(1000) / 1000)
+    plt.gca().yaxis.set_major_formatter(PercentFormatter(1))
+    plt.show()
+
+
+def normal_distribution() -> list[int]:
     with open('normal_distribution.txt') as fin:
         context = fin.read()
-    elements = list(map(int, context.splitlines()))
-    function_pdf(elements)
-    function_cdf(elements)
+    return list(map(int, context.splitlines()))
 
 
 if __name__ == '__main__':
-    normal_distribution()
+    elements = normal_distribution()
+    function_pdf(elements)
+    function_cdf(elements)
+    function_1000_cdf(elements)