grohith327

import pandas as pd 

## Load Iris dataset
df = pd.read_csv('/Users/rohith/Documents/Datasets/Iris_dataset/Iris.csv')
## Retrieve the target values and drop the Id along with it
target = df['Species']
df = df.drop(['Species','Id'],axis=1)
## Drop the two features we won't be using from the dataframe 
df = df.drop(['SepalWidthCm','PetalWidthCm'],axis=1)

grohith327

from sklearn.cluster import KMeans 

clf = KMeans(n_clusters = 3)
clf.fit(train_data)
pred = clf.predict(test_data)

grohith327

pred = []
for point in test_data:
    ## Find distance between test data point and centroids
    dis_point_c1 = ((c1[0]-point[0])**2 + (c1[1]-point[1])**2 + 
                    (c1[2]-point[2])**2 + (c1[3]-point[3])**2)**0.5
    dis_point_c2 = ((c2[0]-point[0])**2 + (c2[1]-point[1])**2 + 
                    (c2[2]-point[2])**2 + (c2[3]-point[3])**2)**0.5
    dis_point_c3 = ((c3[0]-point[0])**2 + (c3[1]-point[1])**2 + 
                    (c3[2]-point[2])**2 + (c3[3]-point[3])**2)**0.5
    ## Find the cluster to which the point is closest to and append 

grohith327

## K-Means Algorithm
import random
import numpy as np
## Randomly place the centroids of the three clusters 
c1 = [float(np.random.randint(4,8)),float(np.random.randint(1,5)),
      float(np.random.randint(1,7)),float(np.random.randint(0,3))]
c2 = [float(np.random.randint(4,8)),float(np.random.randint(1,5)),
      float(np.random.randint(1,7)),float(np.random.randint(0,3))]
c3 = [float(np.random.randint(4,8)),float(np.random.randint(1,5)),
      float(np.random.randint(1,7)),float(np.random.randint(0,3))]

grohith327

import pandas as pd 
import numpy as np
from sklearn.utils import shuffle

## Load Iris dataset
df = pd.read_csv('/Users/rohith/Documents/Datasets/Iris_dataset/iris.csv') 
## Store the target vaue
classes = df['Species']  
## Drop the Id and Class values from dat
df = df.drop(['Id','Species'],axis=1) 

grohith327

from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

clf = SVC(kernel='linear')
clf.fit(x_train,y_train)
y_pred = clf.predict(x_test)
print(accuracy_score(y_test,y_pred))

grohith327

from sklearn.metrics import accuracy_score

## Clip the weights 
index = list(range(10,90))
w1 = np.delete(w1,index)
w2 = np.delete(w2,index)

w1 = w1.reshape(10,1)
w2 = w2.reshape(10,1)
## Extract the test data features 

grohith327

## Support Vector Machine 
import numpy as np

train_f1 = x_train[:,0]
train_f2 = x_train[:,1]

train_f1 = train_f1.reshape(90,1)
train_f2 = train_f2.reshape(90,1)

w1 = np.zeros((90,1))

grohith327

from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
import numpy as np
## Drop rest of the features and extract the target values
df = df.drop(['SepalWidthCm','PetalWidthCm'],axis=1)
Y = []
target = df['Species']
for val in target:
    if(val == 'Iris-setosa'):
        Y.append(-1)

grohith327

import matplotlib.pyplot as plt

x = df['SepalLengthCm']
y = df['PetalLengthCm']

setosa_x = x[:50]
setosa_y = y[:50]

versicolor_x = x[50:]
versicolor_y = y[50:]