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decisiontree.py

+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import math
+import copy
+
+dataset = pd.read_csv('datasetwether.csv')
+X = dataset.iloc[:, 1:].values
+print(X)
+attribute = ['Humidity', 'Wind Bearing (degrees)', 'Pressure (millibars)', 'Loud Cover']
+
+
+class Node(object):
+    def __init__(self):
+        self.value = None
+        self.decision = None
+        self.childs = None
+
+
+def findEntropy(data, rows):
+    yes = 0
+    no = 0
+    ans = -1
+    idx = len(data[0]) - 1
+    entropy = 0
+    for i in rows:
+        if data[i][idx] == 'this year low chance':
+            yes = yes + 1
+        else:
+            no = no + 1
+
+    x = yes/(yes+no)
+    y = no/(yes+no)
+    if x != 0 and y != 0:
+        entropy = -1 * (x*math.log2(x) + y*math.log2(y))
+    if x == 1:
+        ans = 1
+    if y == 1:
+        ans = 0
+    return entropy, ans
+
+
+def findMaxGain(data, rows, columns):
+    maxGain = 0
+    retidx = -1
+    entropy, ans = findEntropy(data, rows)
+    if entropy == 0:
+        if ans == 1:
+            print("hight chance")
+        else:
+            print("")
+        return maxGain, retidx, ans
+
+    for j in columns:
+        mydict = {}
+        idx = j
+        for i in rows:
+            key = data[i][idx]
+            if key not in mydict:
+                mydict[key] = 1
+            else:
+                mydict[key] = mydict[key] + 1
+        gain = entropy
+
+        # print(mydict)
+        for key in mydict:
+            yes = 0
+            no = 0
+            for k in rows:
+                if data[k][j] == key:
+                    if data[k][-1] == 'Yes':
+                        yes = yes + 1
+                    else:
+                        no = no + 1
+            # print(yes, no)
+            x = yes/(yes+no)
+            y = no/(yes+no)
+             #print(x, y)
+            if x != 0 and y != 0:
+                gain += (mydict[key] * (x*math.log2(x) + y*math.log2(y)))/14
+        # print(gain)
+        if gain > maxGain:
+            # print("hello")
+            maxGain = gain
+            retidx = j
+
+    return maxGain, retidx, ans
+
+
+def buildTree(data, rows, columns):
+
+    maxGain, idx, ans = findMaxGain(X, rows, columns)
+    root = Node()
+    root.childs = []
+    print(maxGain
+     )
+    if maxGain == 0:
+        if ans == 1:
+            root.value = 'Yes'
+        else:
+            root.value = 'No'
+        return root
+
+    root.value = attribute[idx]
+    mydict = {}
+    for i in rows:
+        key = data[i][idx]
+        if key not in mydict:
+            mydict[key] = 1
+        else:
+            mydict[key] += 1
+
+    newcolumns = copy.deepcopy(columns)
+    newcolumns.remove(idx)
+    for key in mydict:
+        newrows = []
+        for i in rows:
+            if data[i][idx] == key:
+                newrows.append(i)
+        # print(newrows)
+        temp = buildTree(data, newrows, newcolumns)
+        temp.decision = key
+        root.childs.append(temp)
+    return root
+
+
+def traverse(root):
+    print(root.decision)
+    print(root.value)
+
+    n = len(root.childs)
+    if n > 0:
+        for i in range(0, n):
+            traverse(root.childs[i])
+
+
+def calculate():
+    rows = [i for i in range(0, 14)]
+    columns = [i for i in range(0, 4)]
+    root = buildTree(X, rows, columns)
+    root.decision = 'next year'
+
+    traverse(root)
+    return root.decision + " " + root.value
+
+
+calculate()