ML_recipes_5_FirstClassifier.py

# coding: utf-8

# In[10]:


#import a dataset
from sklearn import datasets
iris=datasets.load_iris()
X=iris.data
y=iris.target

#Train-Test Split
from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.5)


# In[11]:


#Writing KNNClassifier
import random

class KNN():
    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=random.choice(self.y_train)
            predictions.append(label)
        return predictions
    
    
#KNeighbors Classifier
#from sklearn.neighbors import KNeighborsClassifier
my_classifier=KNN()
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))



# In[13]:


from scipy.spatial import distance

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

class KNN():
    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]
#KNeighbors Classifier
#from sklearn.neighbors import KNeighborsClassifier
my_classifier=KNN()
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))


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