grohith327

from sklearn.linear_model import LinearRegression 
from sklearn.metrics import r2_score

clf = LinearRegression(normalize=True)
clf.fit(x_train,y_train)
y_pred = clf.predict(x_test)
print(r2_score(y_test,y_pred))

grohith327

import numpy as np 
from sklearn.cross_validation import train_test_split

## Retrieve features
X = df.values.tolist()
Y = []
## Convert classes in Strings to Integers 
for val in target:
    if(val == 'Iris-setosa'):
        Y.append(0)

grohith327

from simplegan.gan import Pix2Pix
## Create an object
gan = Pix2Pix() ## Customize the model by specifying parameters for Pix2Pix object
## Load the training and testing data
train_ds, test_ds = gan.load_data(use_edges2handbags = True, batch_size = 32)
## Get samples from training data
train_samples = gan.get_sample(data= train_ds, n_samples = 2)
## Get samples from testing data
train_samples = gan.get_sample(data= test_ds, n_samples = 2)
## train the model

grohith327

from simplegan.autoencoder import ConvolutionalAutoencoder
## Create an object
autoenc = ConvolutionalAutoencoder() ## Modify the architecture of the model by specifying parameters
## Load the MNIST data
train_ds, test_ds = autoenc.load_data(use_mnsist = True)
## Get samples from the loaded training data to view them
train_samples = autoenc.get_sample(data = train_ds, n_samples = 2)
## Get samples from the loaded testing data to view them
test_samples = autoenc.get_sample(data = test_ds, n_samples = 2)
## Train the model

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

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:]

grohith327

import cv2

cam = cv2.VideoCapture(0)

cv2.namedWindow("take a picture") 
img_counter = 0
while True:
  ret, frame = cam.read()
  cv2.imshow("test", frame)
  if not ret:

grohith327

import os

os.system('top')

grohith327

import webbrowser

webbrowser.open('http://google.com')

grohith327

import osascript

vol = osascript.osascript('get volume settings')
cur_vol = int(vol[1].split(':')[1].split(',')[0])
cur_vol = cur_vol + 20
if(cur_vol > 100):
  cur_vol = 100
osascript.osascript("set volume output volume "+str(cur_vol))