Chiraagkv

def plot_histories(*args):
  '''
  Plots histories
  '''
  epochs1 = np.arange(1, len(pd.DataFrame(args[0].history)) + 1)
  fig, ax = plt.subplots(len(args), 2, figsize=(16, 7 * len(args)))
  fig.suptitle('Comparing Model Histories')
  for i in range(len(args)):
    loss1 = pd.DataFrame(args[i].history["loss"])
    ax[i, 0].set_title(f"Model {i + 1} Loss")

Chiraagkv

class LinearRegressor:
    def __init__(self, iters, X, y, lr):
        self.iters = iters
        self.X = X
        self.lr = lr
        self.y = y
        self.b = 0
        self.w = 1
    def hypothesis_function(self, x):
        list_of_hypothesis_functions = []

Chiraagkv

import numpy as np
def hypothesis_function(θ0, θ1, X):
  y = θ0 + θ1 * np.array(X)
  return y

Chiraagkv

def cost_function(y_preds, y_true):
    return sum((y_preds - y_true) ** 2) / len(y_preds)

Chiraagkv

def gradient_descent(θ0, θ1, x, y, lr):
  new_weights = []
  y_preds = θ0 + θ1 * x
  new_weights.append(θ0 - lr * (2 / len(y)) * sum(y_preds - y))
  new_weights.append(θ1 - lr * sum((y_preds - y) * x))
  return new_weights

Chiraagkv

import numpy as np
class LinearRegressor():
    def __init__(self):
        self.θ0 = np.random.random((1,))[0]
        self.θ1 = np.random.random((1,))[0]
    
    def predict(self, x):
        return self.θ0 + self.θ1 * np.array(x)
    
    def compute_loss(self, y_preds, y_true):

Chiraagkv

np.random.seed(42)
area = np.array([round(i, 2) for i in np.abs(np.random.random(size=(1000, ))) * 2000])
age = np.array([round(i, 2) for i in np.abs(np.random.random(size=(1000, ))) * 20])
price = area * 100 + age * 30
data = pd.DataFrame({"Area": area, "Age": age, "Price": price})
data

Chiraagkv

import numpy as np
def hypothesis_function(θ, x):
  return np.matmul(np.array(θ).T, np.array(x))