Remove code with side effects from main

fuzzy_logic/fuzzy_operations.py

@@ -6,97 +6,97 @@
 Python:
   - 3.5
 """
-# Create universe of discourse in python using linspace ()
 import numpy as np
-
-X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
-
-# Create two fuzzy sets by defining any membership function (trapmf(), gbellmf(),gaussmf(), etc).
 import skfuzzy as fuzz
 
-abc1 = [0, 25, 50]
-abc2 = [25, 50, 75]
-young = fuzz.membership.trimf(X, abc1)
-middle_aged = fuzz.membership.trimf(X, abc2)
-
-# Compute the different operations using inbuilt functions.
-one = np.ones(75)
-zero = np.zeros((75,))
-# 1. Union = max(µA(x), µB(x))
-union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
-# 2. Intersection = min(µA(x), µB(x))
-intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
-# 3. Complement (A) = (1- min(µA(x))
-complement_a = fuzz.fuzzy_not(young)
-# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
-difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
-# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
-alg_sum = young + middle_aged - (young * middle_aged)
-# 6. Algebraic Product = (µA(x) * µB(x))
-alg_product = young * middle_aged
-# 7. Bounded Sum = min[1,(µA(x), µB(x))]
-bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
-# 8. Bounded difference = min[0,(µA(x), µB(x))]
-bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
-
-# max-min composition
-# max-product composition
-
-
-# Plot each set A, set B and each operation result using plot() and subplot().
-import matplotlib.pyplot as plt
-
-plt.figure()
-
-plt.subplot(4, 3, 1)
-plt.plot(X, young)
-plt.title("Young")
-plt.grid(True)
-
-plt.subplot(4, 3, 2)
-plt.plot(X, middle_aged)
-plt.title("Middle aged")
-plt.grid(True)
-
-plt.subplot(4, 3, 3)
-plt.plot(X, union)
-plt.title("union")
-plt.grid(True)
-
-plt.subplot(4, 3, 4)
-plt.plot(X, intersection)
-plt.title("intersection")
-plt.grid(True)
-
-plt.subplot(4, 3, 5)
-plt.plot(X, complement_a)
-plt.title("complement_a")
-plt.grid(True)
-
-plt.subplot(4, 3, 6)
-plt.plot(X, difference)
-plt.title("difference a/b")
-plt.grid(True)
-
-plt.subplot(4, 3, 7)
-plt.plot(X, alg_sum)
-plt.title("alg_sum")
-plt.grid(True)
-
-plt.subplot(4, 3, 8)
-plt.plot(X, alg_product)
-plt.title("alg_product")
-plt.grid(True)
-
-plt.subplot(4, 3, 9)
-plt.plot(X, bdd_sum)
-plt.title("bdd_sum")
-plt.grid(True)
-
-plt.subplot(4, 3, 10)
-plt.plot(X, bdd_difference)
-plt.title("bdd_difference")
-plt.grid(True)
-
-plt.subplots_adjust(hspace=0.5)
-plt.show()
+
+if __name__ == "__main__":
+    # Create universe of discourse in python using linspace ()
+    X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
+
+    # Create two fuzzy sets by defining any membership function (trapmf(), gbellmf(),gaussmf(), etc).
+    abc1 = [0, 25, 50]
+    abc2 = [25, 50, 75]
+    young = fuzz.membership.trimf(X, abc1)
+    middle_aged = fuzz.membership.trimf(X, abc2)
+
+    # Compute the different operations using inbuilt functions.
+    one = np.ones(75)
+    zero = np.zeros((75,))
+    # 1. Union = max(µA(x), µB(x))
+    union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
+    # 2. Intersection = min(µA(x), µB(x))
+    intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
+    # 3. Complement (A) = (1- min(µA(x))
+    complement_a = fuzz.fuzzy_not(young)
+    # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
+    difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
+    # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
+    alg_sum = young + middle_aged - (young * middle_aged)
+    # 6. Algebraic Product = (µA(x) * µB(x))
+    alg_product = young * middle_aged
+    # 7. Bounded Sum = min[1,(µA(x), µB(x))]
+    bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
+    # 8. Bounded difference = min[0,(µA(x), µB(x))]
+    bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
+
+    # max-min composition
+    # max-product composition
+
+    # Plot each set A, set B and each operation result using plot() and subplot().
+    import matplotlib.pyplot as plt
+
+    plt.figure()
+
+    plt.subplot(4, 3, 1)
+    plt.plot(X, young)
+    plt.title("Young")
+    plt.grid(True)
+
+    plt.subplot(4, 3, 2)
+    plt.plot(X, middle_aged)
+    plt.title("Middle aged")
+    plt.grid(True)
+
+    plt.subplot(4, 3, 3)
+    plt.plot(X, union)
+    plt.title("union")
+    plt.grid(True)
+
+    plt.subplot(4, 3, 4)
+    plt.plot(X, intersection)
+    plt.title("intersection")
+    plt.grid(True)
+
+    plt.subplot(4, 3, 5)
+    plt.plot(X, complement_a)
+    plt.title("complement_a")
+    plt.grid(True)
+
+    plt.subplot(4, 3, 6)
+    plt.plot(X, difference)
+    plt.title("difference a/b")
+    plt.grid(True)
+
+    plt.subplot(4, 3, 7)
+    plt.plot(X, alg_sum)
+    plt.title("alg_sum")
+    plt.grid(True)
+
+    plt.subplot(4, 3, 8)
+    plt.plot(X, alg_product)
+    plt.title("alg_product")
+    plt.grid(True)
+
+    plt.subplot(4, 3, 9)
+    plt.plot(X, bdd_sum)
+    plt.title("bdd_sum")
+    plt.grid(True)
+
+    plt.subplot(4, 3, 10)
+    plt.plot(X, bdd_difference)
+    plt.title("bdd_difference")
+    plt.grid(True)
+
+    plt.subplots_adjust(hspace=0.5)
+    plt.show()

machine_learning/polymonial_regression.py

@@ -36,8 +36,9 @@ def viz_polymonial():
     return
 
 
-viz_polymonial()
+if __name__ == "__main__":
+    viz_polymonial()
 
-# Predicting a new result with Polymonial Regression
-pol_reg.predict(poly_reg.fit_transform([[5.5]]))
-# output should be 132148.43750003
+    # Predicting a new result with Polymonial Regression
+    pol_reg.predict(poly_reg.fit_transform([[5.5]]))
+    # output should be 132148.43750003