Add files via upload

Author: arnab39

Decision Tree Code/ID3_DecisionTree.py

@@ -0,0 +1,115 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Aug 27 14:58:11 2017
+
+@author: hp pc
+"""
+
+import scipy.io as sio
+import random
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import copy
+
+a = sio.loadmat('data.mat')
+b = a['h']
+
+df = pd.DataFrame(b,columns=list('xy'))
+colmap = {1: 'r', 2: 'g', 3: 'b', 4: 'm', 5: 'y', 6: 'k'}
+
+euc_error = pd.DataFrame({'2': [0],'3': [0],'4': [0],'5': [0],'6': [0]})
+cos_error = pd.DataFrame({'2': [0],'3': [0],'4': [0],'5': [0],'6': [0]})
+
+def plotting(df, centroids, colmap):
+# fig = plt.figure(figsize=(5, 5))
+ plt.scatter(df['x'], df['y'], color=df['color'], alpha=0.2, edgecolor='k')
+ for i in centroids.keys():
+ plt.scatter(*centroids[i], color=colmap[i])
+ plt.xlim(-1, 5)
+ plt.ylim(-2, 3)
+ plt.show()
+
+
+def dist_calc(x1, x2, y1, y2, n):
+ dist = 0
+ if n == 0:
+ dist = ((x1-x2)**2 + (y1-y2)**2)**0.5
+ elif n ==1:
+ dist = 1 - ((x1*x2 + y1*y2)/(np.sqrt((x1**2 + y1**2)*(x2**2 + y2**2))))
+ return dist
+
+def init_centroid(k):
+ np.random.seed(200)
+ centroids = {
+ i+1: [random.uniform(-0.3, 4.0), random.uniform(-1.4, 2.0)]
+ for i in range(k)
+ }
+ print(centroids)
+ return centroids
+ 
+def first_assignment(df, centroids, colmap, dist_type):
+ for i in centroids.keys():
+ df['distance_from_{}'.format(i)] = dist_calc(df['x'], centroids[i][0], df['y'], centroids[i][1], dist_type)
+ 
+ centroid_distance_cols = ['distance_from_{}'.format(i) for i in centroids.keys()]
+ df['closest'] = df.loc[:, centroid_distance_cols].idxmin(axis=1)
+ df['closest'] = df['closest'].map(lambda x: int(x.lstrip('distance_from_')))
+ df['color'] = df['closest'].map(lambda x: colmap[x])
+ return df
+
+def update_centroid(df, centroids):
+ for i in centroids.keys():
+ centroids[i][0] = np.mean(df[df['closest'] == i]['x'])
+ centroids[i][1] = np.mean(df[df['closest'] == i]['y'])
+ return centroids
+
+def clustering(df, k, colmap, dist_type):
+ centroids = init_centroid(k)
+ i = 0
+ while(1):
+ prev_set = centroids
+ print(prev_set)
+ df = first_assignment(df, centroids, colmap, dist_type)
+ centroids = update_centroid(df, centroids)
+ print(centroids)
+ i=i+1;
+ print("Iteration"+str(i))
+ if prev_set==centroids:
+ break
+ return centroids
+
+def error_calc(df):
+ e_euc =[]
+ e_cos =[]
+ k = [2,3,4,5,6]
+ 
+ for i in range(2,7):
+ for j in range(len(df.index)):
+ euc_error['{}'.format(i)] += df['distance_from_{}'.format(df['closest'][j])][j]
+ for j in range(len(df.index)):
+ cos_error['{}'.format(i)] += df['distance_from_{}'.format(df['closest'][j])][j]
+ e_euc.append(euc_error['{}'.format(i)])
+ print(euc_error['{}'.format(i)])
+ e_cos.append(cos_error['{}'.format(i)])
+ print(cos_error['{}'.format(i)])
+ plt.plot(k,e_euc)
+ plt.plot(k,e_cos)
+ plt.xlabel('Number of Clusters')
+ plt.ylabel('Error')
+ plt.title('Error variation with number of clusters')
+ return df
+
+def k_means(df, k, colmap, dist_type):
+ centroids = clustering(df, k, colmap, dist_type)
+ plotting(df, centroids, colmap)
+
+
+k = 4
+dist_type = 1
+
+k_means(df, k, colmap, dist_type)
+
+#df = error_calc(df)
+
+