In [3]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
In [58]:
class linear_regression():
def __init__(self):
self.weights = None
self.learning_rate = None
self.epochs = None
self.trainx = None
self.trainy = None
self.costcurve = []
print('Status: Model Initialized')
def train(self, trainx, trainy, learning_rate, epochs):
# np.random.seed(10)
self.trainx = trainx
self.trainy = trainy
self.learning_rate = learning_rate
self.epochs = epochs
# self.weights = np.random.randn(self.trainx.shape[1]+1)
self.weights = np.random.uniform(low=0.0, high=inputx.shape[1]**0.5, size=inputx.shape[1]+1)
self.trainx = np.append(self.trainx,np.ones((self.trainx.shape[0],1)), axis=1)
for epoch in range(epochs):
output = np.dot(self.trainx, self.weights)
output = np.reshape(output, (output.shape[0],1))
error = np.subtract(self.trainy, output)
total_error = np.sum(error)
cost = np.mean(error**2)
self.costcurve.append([epoch+1, cost])
gradients = (self.learning_rate / self.trainx.shape[0]) * np.dot(error.T, self.trainx)
gradients = np.reshape(gradients, (gradients.T.shape[0],))
self.weights += gradients
print('step:{0} \n cost:{1}'.format(epoch+1, cost))
# return self.weights
def plotCostCurve(self):
costcurvearray = np.array(self.costcurve)
plt.plot(costcurvearray[:,0],costcurvearray[:,1])
plt.xlabel('Epochs')
plt.ylabel('Cost')
plt.show()
def predict(self, validatex):
validatex_new = np.append(validatex,np.ones((validatex.shape[0],1)), axis=1)
predict = np.dot(validatex_new, self.weights)
return np.reshape(predict, (predict.shape[0],1))
We are using the MPG dataset from UCI Datasets to test our implementation. http://archive.ics.uci.edu/ml/datasets/Auto+MPG
In [76]:
mpg_data = np.genfromtxt('mpg.txt', delimiter=',', dtype='float')
print(mpg_data.shape)
mpg_data = mpg_data[~np.isnan(mpg_data).any(axis=1)]
inputx = mpg_data[:,1:8]
for i in range(inputx.shape[1]):
inputx[:,i] = (inputx[:,i]-np.min(inputx[:,i]))/(np.max(inputx[:,i])-np.min(inputx[:,i]))
inputy = np.reshape(mpg_data[:,0],(mpg_data.shape[0],1))
(398, 7)
In [81]:
model = linear_regression()
model.train(inputx, inputy, 0.01, 1000)
Status: Model Initialized
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In [82]:
print(model.weights)
[ -3.17834243 -2.92434162 -1.5453997 -3.18214353 9.55776345
12.13318755 16.99328664]
In [83]:
model.plotCostCurve()
In [80]:
model.predict(inputx)
Out[80]:
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Content source: y2ee201/Deep-Learning-Nanodegree
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