def randomLine():
seed(1)
alpha = 0.000003
numOfSteps = 1000
numOfLoops = 250
model = LinearRegression()
#generating feature x
x = np.random.uniform(low=-4, high=6, size=(500, ))
#generating coeffient and constant
a = float(np.random.uniform(low=-5, high=10, size=(1, )))
b = float(np.random.uniform(low=-5, high=5, size=(1, )))
#adding gaussian noise
noise = np.random.normal(0, 1, 500)
# labels
y = []
for i in range(len(x)):
t = a * x[i] + b + noise[i]
y.append(t)
for i in range(len(x)):
model.addSample(x[i], y[i])
Samples = model.getSamples()
Labels = model.getValues()
for i in range(numOfLoops + 1):
model.fit(alpha, numOfSteps)
plt.show()
def setPlane():
alpha = 0.000000003
numOfSteps = 1000
numOfLoops = 10
model = LinearRegression()
noOfFeatures = 2
x = list(range(0, 1000))
#calculating points for points((x,2x),5x)and ((2x,x),4x)
x1, y1 = x, [i * 2 for i in x]
x2, y2 = [i * 2 for i in x], x
x = x1 + x2
y = y1 + y2
#calculating z = x + 2y
z = [(x[i] + 2 * y[i]) for i in range(len(x))]
#array of features as (x1,x2)
features = list(zip(x, y))
for i in range(len(z)):
model.addSample(features[i], z[i])
Samples = model.getSamples(noOfFeatures)
Labels = model.getValues()
for i in range(numOfLoops + 1):
current_Hypothesis, cost = model.fit(alpha, numOfSteps)
print("Current hypothesis: ", current_Hypothesis, ", cost = ",
"{0:.4f}".format(cost))
def rabndomLine():
seed(1)
alpha = 0.003
numOfSteps = 1000
numOfLoops = 100
noise = 20
noOfFeatures = 2
model = LinearRegression()
x = []
#generationg the values for variables given in assignment
x1 = np.random.uniform(low=0, high=1, size=(5000, ))
x2 = np.random.uniform(low=0, high=1, size=(5000, ))
#generationg the values for coeffients given in assignment
a = float(np.random.uniform(low=-100, high=100, size=(1, )))
b = float(np.random.uniform(low=-100, high=100, size=(1, )))
c = float(np.random.uniform(low=-20, high=20, size=(1, )))
#generationg the value for noise given in assignment
delta = np.random.uniform(low=-100, high=100, size=(5000, ))
#calculating the label
y = [(a * x1[i] + b * x2[i] + c + delta[i]) for i in range(len(x1))]
#array of features as (x1,x2)
features = list(zip(x1, x2))
for i in range(len(y)):
model.addSample(features[i], y[i])
Samples = model.getSamples(noOfFeatures)
Labels = model.getValues()
for i in range(numOfLoops + 1):
current_Hypothesis, cost = model.fit(alpha, numOfSteps)
print("Current hypothesis: ", current_Hypothesis, ", cost = ",
"{0:.4f}".format(cost))
def randomDimension(noOfFeatures):
seed(1)
alpha = 0.00003
numOfSteps = 1000
numOfLoops = 100
noise = 20
model = LinearRegression()
examples = 5000
#array of coefficient
t = []
#array of all features of each 5000 examples
x = []
# array of r - label
r = []
#array of product of coefficient and x for each dimention
tx = []
#array of features as (x1,x2...xn)
features = []
#generating random variable for each coefficient t0 in assignment
t0 = float(np.random.uniform(low=-100, high=100, size=(1, )))
for i in range(1, noOfFeatures + 1):
#generating random variable for each coefficient t in assignment
a = float(np.random.uniform(low=-100, high=100, size=(1, )))
t.append(a)
#generating random variable for each feature x in assignment
x1 = np.random.uniform(low=0, high=1, size=(5000, ))
x.append(list(x1))
#poduct of coefficient and x
p = list(a * x1)
tx.append(p)
for j in range(examples):
#summation of poduct of coefficient and x for each sample
z = sum(i[j] for i in tx)
#adding noise and t0 to find r to the summation
y = z + t0 + noise
r.append(y)
g = [i[j] for i in x]
features.append(g)
for i in range(len(r)):
model.addSample(features[i], r[i])
Samples = model.getSamples(noOfFeatures)
Labels = model.getValues()
for i in range(numOfLoops + 1):
current_Hypothesis, cost = model.fit(alpha, numOfSteps)
print("Current hypothesis: ", current_Hypothesis, ", cost = ",
"{0:.4f}".format(cost))
def setLine():
alpha = 0.000000003
numOfSteps = 100
numOfLoops = 50
model = LinearRegression()
#feature
x = list(range(0, 1000))
#label
y = x
for i in x:
y = i
model.addSample(i, y)
Samples = model.getSamples()
Labels = model.getValues()
for i in range(numOfLoops + 1):
model.fit(alpha, numOfSteps)
plt.show()
def setLine():
alpha = 0.000000003
numOfSteps = 100
numOfLoops = 50
model = LinearRegression()
#feature
x = list(range(0, 1000))
#label
y = x
for i in x:
y = i
model.addSample(i, y)
Samples = model.getSamples()
Labels = model.getValues()
for i in range(numOfLoops + 1):
current_Hypothesis, cost = model.fit(alpha, numOfSteps)
print("Current hypothesis: ", current_Hypothesis, ", cost = ",
"{0:.4f}".format(cost))