iris_selfclassifier.py

import random
from scipy.spatial import distance

def euc(a,b):
    return distance.euclidean(a,b)

#self written classifier
 
class ScappyKNN():
    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 = euc(row,self.X_train[0])
        best_index = 0

        for i in range(1, len(self.X_train)):
            dist = euc (row, self.X_train[i])
            if dist < best_dist:
                best_dist = dist
                best_index = i
        return self.y_train[best_index]




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_classifier = tree.DecisionTreeClassifier()

# from sklearn.neighbors import KNeighborsClassifier
my_classifier = ScappyKNN()


my_classifier.fit(x_train,y_train)

predictions = my_classifier.predict(x_test)

from sklearn.metrics import accuracy_score

print accuracy_score(y_test, predictions)