import numpy as np

import matplotlib.pyplot as plt

plt.style.use('./deeplearning.mplstyle')

# x_train is the input variable (size in 1000 square feet)

# y_train is the target (price in 1000s of dollars)

x_train = np.array([1.0, 2.0])

y_train = np.array([300.0, 500.0])

print(f"x_train = {x_train}")

print(f"y_train = {y_train}")

# m is the number of training examples

print(f"x_train.shape: {x_train.shape}")

m = x_train.shape[0]

print(f"Number of training examples is: {m}")

# m is the number of training examples

m = len(x_train)

print(f"Number of training examples is: {m}")

i = 0 # Change this to 1 to see (x^1, y^1)

x_i = x_train[i]

y_i = y_train[i]

print(f"(x^({i}), y^({i})) = ({x_i}, {y_i})")

# Plot the data points

plt.scatter(x_train, y_train, marker='x', c='r')

# Set the title

plt.title("Housing Prices")

# Set the y-axis label

plt.ylabel('Price (in 1000s of dollars)')

# Set the x-axis label

plt.xlabel('Size (1000 sqft)')

plt.show()

w = 100

b = 100

print(f"w: {w}")

print(f"b: {b}")

def compute_model_output(x, w, b):

    """

    Computes the prediction of a linear model

    Args:

      x (ndarray (m,)): Data, m examples 

      w,b (scalar)    : model parameters  

    Returns

      f_wb (ndarray (m,)): model prediction

    """

    m = x.shape[0]

    f_wb = np.zeros(m)

    for i in range(m):

        f_wb[i] = w * x[i] + b

        

    return f_wb

tmp_f_wb = compute_model_output(x_train, w, b,)

# Plot our model prediction

plt.plot(x_train, tmp_f_wb, c='b',label='Our Prediction')

# Plot the data points

plt.scatter(x_train, y_train, marker='x', c='r',label='Actual Values')

# Set the title

plt.title("Housing Prices")

# Set the y-axis label

plt.ylabel('Price (in 1000s of dollars)')

# Set the x-axis label

plt.xlabel('Size (1000 sqft)')

plt.legend()

plt.show()

w = 200                         

b = 100    

x_i = 1.2

cost_1200sqft = w * x_i + b    

print(f"${cost_1200sqft:.0f} thousand dollars")

w = 200

b= 100

x_i = 1.2

cost_1200