Update README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>

scipy/scipy.stats.fisher_exact/README.md

@@ -77,7 +77,6 @@ If network architectures are tested, typically, the same data set is used in bot
 
 This translates in to the table: [[7, 17], [15, 5]]
 
-
 ```python
 from scipy.stats import fisher_exact
 
@@ -86,5 +85,104 @@ print(res.statistic) # -> 0.13725490196078433
 print(res.pvalue) # -> 0.0028841933752349743
 ```
 
+## Network performance analysis
+
+![image1](image1.png)
+
+```python
+from scipy.stats import fisher_exact
+import numpy as np
+import matplotlib.pyplot as plt
+
+N: int = 10000
+correct_a: int = N // 2
+
+values = np.arange(0, N + 1, 100)
+results_less = np.zeros((values.shape[0]))
+results_greater = np.zeros((values.shape[0]))
+results_two_sided = np.zeros((values.shape[0]))
 
 
+for i in range(0, values.shape[0]):
+    correct_b: int = int(values[i])
+    res = fisher_exact(
+        [[N - correct_a, N - correct_b], [correct_a, correct_b]], alternative="less"
+    )
+    results_less[i] = res.pvalue
+
+for i in range(0, values.shape[0]):
+    correct_b = int(values[i])
+    res = fisher_exact(
+        [[N - correct_a, N - correct_b], [correct_a, correct_b]], alternative="greater"
+    )
+    results_greater[i] = res.pvalue
+
+for i in range(0, values.shape[0]):
+    correct_b = int(values[i])
+    res = fisher_exact(
+        [[N - correct_a, N - correct_b], [correct_a, correct_b]],
+        alternative="two-sided",
+    )
+    results_two_sided[i] = res.pvalue
+
+
+plt.plot(100.0 * values / N, results_two_sided, label="two-sided")
+plt.plot(100.0 * values / N, results_less, label="less")
+plt.plot(100.0 * values / N, results_greater, label="greater")
+
+plt.title(f"Compared to a performance A of {100.0 * correct_a /N}%")
+plt.ylabel("p-value")
+plt.xlabel("Correct [%]")
+plt.legend()
+plt.show()
+```
+
+![image2](image2.png)
+
+```python
+from scipy.stats import fisher_exact
+import numpy as np
+import matplotlib.pyplot as plt
+
+N: int = 10000
+correct_a: int = int(N * 0.99)
+
+values = np.arange(int(N * 0.98), N + 1)
+results_less = np.zeros((values.shape[0]))
+results_greater = np.zeros((values.shape[0]))
+results_two_sided = np.zeros((values.shape[0]))
+
+
+for i in range(0, values.shape[0]):
+    correct_b: int = int(values[i])
+    res = fisher_exact(
+        [[N - correct_a, N - correct_b], [correct_a, correct_b]], alternative="less"
+    )
+    results_less[i] = res.pvalue
+
+for i in range(0, values.shape[0]):
+    correct_b = int(values[i])
+    res = fisher_exact(
+        [[N - correct_a, N - correct_b], [correct_a, correct_b]], alternative="greater"
+    )
+    results_greater[i] = res.pvalue
+
+for i in range(0, values.shape[0]):
+    correct_b = int(values[i])
+    res = fisher_exact(
+        [[N - correct_a, N - correct_b], [correct_a, correct_b]],
+        alternative="two-sided",
+    )
+    results_two_sided[i] = res.pvalue
+
+
+plt.plot(100.0 * values / N, results_two_sided, label="two-sided")
+plt.plot(100.0 * values / N, results_less, label="less")
+plt.plot(100.0 * values / N, results_greater, label="greater")
+
+plt.title(f"Compared to a performance A of {100.0 * correct_a /N}%")
+plt.ylabel("p-value")
+plt.xlabel("Correct [%]")
+plt.legend()
+plt.show()
+```