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6.py
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6.py
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from scipy.spatial import distance
def ecu(a,b):
return distance.euclidean(a,b)
class scrpknn():
def fit(self, x_train, y_train):
self.x_train = x_train
self.y_train = y_train
def predict(self,x_test):
predictions =[]
for row in x_test:
label = self.closest(row)
predictions.append(label)
return predictions
def closest(self, row):
best_dist = ecu(row, self.x_train[0])
best_index = 0
for i in range(1,len(self.x_train)):
dist = ecu(row,self.x_train[i])
if dist < best_dist:
best_dist = dist
best_index = i
return self.y_train[best_index]
import numpy as np
from sklearn import datasets
iris = datasets.load_iris()
x= iris.data
y= iris.target
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size =.5)
from sklearn import tree
#my_cls = tree.DecisionTreeClassifier()
my_cls = scrpknn()
my_cls.fit(x_train,y_train)
predictions = my_cls.predict(x_test)
print predictions
from sklearn.metrics import accuracy_score
print accuracy_score(y_test,predictions)