from gradient_descent_funcs import gradient_descent
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("heights.csv")
X = df["height"]
y = df["weight"]
b, m = gradient_descent(X, y, num_iterations=1000, learning_rate=0.0001)
y_predictions = [m * x + b for x in X]
new_y = [element * m + b for element in y]
plt.plot(X, y, 'o')
#plot your line here:
plt.plot(X, y_predictions)
plt.show()
return m_gradient
def step_gradient(b_current, m_current, x, y,learning_rate):
b_gradient = get_gradient_at_b(x, y, b_current, m_current)
m_gradient = get_gradient_at_m(x, y, b_current, m_current)
b = b_current - (learning_rate * b_gradient)
m = m_current - (learning_rate * m_gradient)
return [b, m]
def gradient_descent(x,y,learning_rate,num_iterations):
b=0
m=0
for i in range(num_iterations):
b,m=step_gradient(b,m,x,y,learning_rate)
return b,m
months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
revenue = [52, 74, 79, 95, 115, 110, 129, 126, 147, 146, 156, 184]
b, m = gradient_descent(months, revenue, 0.01, 1000)
y = [m*x + b for x in months]
plt.plot(months, revenue, "o")
plt.plot(months, y)
plt.show()import codecademylib3_seaborn
import matplotlib.pyplot as plt
def get_gradient_at_b(x, y, b, m):
N = len(x)
diff = 0
for i in range(N):
x_val = x[i]
y_val = y[i]
diff += (y_val - ((m * x_val) + b))
b_gradient = -(2/N) * diff
return b_gradient
def get_gradient_at_m(x, y, b, m):
import codecademylib3_seaborn
from gradient_descent_funcs import gradient_descent
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("heights.csv")
X = df["height"]
y = df["weight"]
plt.plot(X, y, 'o')
#plot your line here:
[b,m]=gradient_descent(X,y,num_iterations=1000,learning_rate=0.0001)
y_predictions= [x*m+b for x in X]
plt.plot(X,y_predictions)
plt.show()
>>>>Other file also attached
import codecademylib3_seaborn
import matplotlib.pyplot as plt
def get_gradient_at_b(x, y, b, m):
N = len(x)
diff = 0
for i in range(N):
x_val = x[i]
y_val = y[i]
diff += (y_val - ((m * x_val) + b))
b_gradient = -(2/N) * diff
return b_gradient
def get_gradient_at_m(x, y, m, b):
N = len(x)
diff = 0
for i in range(len(x)):
diff += x[i]* (y[i] - (m*x[i] + b))
m_gradient = -2/N * diff
return m_gradient
def get_gradient_at_b(x, y, b, m):
N = len(x)
diff = 0
for i in range(N):
x_val = x[i]
y_val = y[i]
diff += (y_val - ((m * x_val) + b))
b_gradient = -(2/N) * diff
return b_gradient
def get_gradient_at_m(x, y, b, m):
N = len(x)
diff = 0
for i in range(N):
x_val = x[i]
y_val = y[i]
diff += x_val * (y_val - ((m * x_val) + b))
m_gradient = -(2/N) * diff
return m_gradient
#Your step_gradient function here
def step_gradient(x, y, b_current, m_current):
b_gradient = get_gradient_at_b(x, y, b_current, m_current)
m_gradient = get_gradient_at_m(x, y, b_current, m_current)
b = b_current - (0.01 * b_gradient)
m = m_current - (0.01 * m_gradient)
return [b, m]
months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
revenue = [52, 74, 79, 95, 115, 110, 129, 126, 147, 146, 156, 184]
# current intercept guess:
b = 0
# current slope guess:
m = 0
b, m = step_gradient(months, revenue, b, m)
print(b, m)
#Convergence
import codecademylib3_seaborn
import matplotlib.pyplot as plt
from data import bs, bs_000000001, bs_01
iterations = range(1400)
num_iterations = 800
convergence_b = 47
plt.plot(iterations, bs)
plt.xlabel("Iterations")
plt.ylabel("b value")
plt.show()
#Learning rate
#Means how much to change of a model in respond to the change of outer error
import codecademylib3_seaborn
import matplotlib.pyplot as plt
from data import bs, bs_000000001, bs_01
iterations = range(100)
num_iterations = 800
convergence_b = 47
plt.plot(iterations, bs_01)
plt.xlabel("Iterations")
plt.ylabel("b value")
plt.show()
import codecademylib3_seaborn
import matplotlib.pyplot as plt
def get_gradient_at_b(x, y, b, m):
N = len(x)
diff = 0
for i in range(N):
x_val = x[i]
y_val = y[i]
diff += (y_val - ((m * x_val) + b))
b_gradient = -(2/N) * diff
return b_gradient
def get_gradient_at_m(x, y, b, m):
N = len(x)
diff = 0
for i in range(N):
x_val = x[i]
y_val = y[i]
diff += x_val * (y_val - ((m * x_val) + b))
m_gradient = -(2/N) * diff
return m_gradient
#Your step_gradient function here
def step_gradient(b_current, m_current, x, y, learning_rate):
b_gradient = get_gradient_at_b(x, y, b_current, m_current)
m_gradient = get_gradient_at_m(x, y, b_current, m_current)
b = b_current - (learning_rate * b_gradient)
m = m_current - (learning_rate * m_gradient)
return [b, m]
#Your gradient_descent function here:
def gradient_descent(x, y, learning_rate, num_iterations):
b = 0
m = 0
for i in range(num_iterations):
b, m = step_gradient(b, m, x, y, learning_rate)
return [b,m]
months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
revenue = [52, 74, 79, 95, 115, 110, 129, 126, 147, 146, 156, 184]
#Uncomment the line below to run your gradient_descent function
b, m = gradient_descent(months, revenue, 0.01, 1000)
#Uncomment the lines below to see the line you've settled upon!
y = [m*x + b for x in months]
plt.plot(months, revenue, "o")
plt.plot(months, y)
plt.show()
import codecademylib3_seaborn
from gradient_descent_funcs import gradient_descent
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("heights.csv")
X = df["height"]
y = df["weight"]
b, m = gradient_descent(X, y, num_iterations = 1000, learning_rate = 0.0001)
y_predictions = [value * m + b for value in X]
plt.plot(X, y, 'o')
#plot your line here:
plt.plot(X, y_predictions, 'o')
plt.show()
import codecademylib3_seaborn
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
temperature = np.array(range(60, 100, 2))
temperature = temperature.reshape(-1, 1)
sales = [65, 58, 46, 45, 44, 42, 40, 40, 36, 38, 38, 28, 30, 22, 27, 25, 25, 20, 15, 5]
line_fitter = LinearRegression()
line_fitter.fit(temperature, sales)
sales_predict = line_fitter.predict(temperature)
plt.plot(temperature, sales, 'o')
plt.plot(temperature, sales_predict)
plt.show()
months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
revenue = [52, 74, 79, 95, 115, 110, 129, 126, 147, 146, 156, 184]
# current intercept guess:
b = 0
# current slope guess:
m = 0
# Call your function here to update b and m
b, m = step_gradient(months, revenue, b, m)
print(b, m)
#------------------
#linear regression on real data
import codecademylib3_seaborn
from gradient_descent_funcs import gradient_descent
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("heights.csv")
X = df["height"]
y = df["weight"]
b, m = gradient_descent(X, y, 0.0001, 1000)
plt.plot(X, y, 'o')
#plot your line here:
y_predictions = [height * m + b for height in X]
plt.plot(X, y_predictions)
plt.show()