""" 
lab_utils_common.py
    functions common to all optional labs, Course 1, Week 2 
"""

import numpy as np
import matplotlib.pyplot as plt

plt.style.use('./deeplearning.mplstyle')
dlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0';
dlcolors = [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]
dlc = dict(dlblue = '#0096ff', dlorange = '#FF9300', dldarkred='#C00000', dlmagenta='#FF40FF', dlpurple='#7030A0')


##########################################################
# Regression Routines
##########################################################

#Function to calculate the cost
def compute_cost_matrix(X, y, w, b, verbose=False):
    """
    Computes the gradient for linear regression
     Args:
      X (ndarray (m,n)): Data, m examples with n features
      y (ndarray (m,)) : target values
      w (ndarray (n,)) : model parameters  
      b (scalar)       : model parameter
      verbose : (Boolean) If true, print out intermediate value f_wb
    Returns
      cost: (scalar)
    """
    m = X.shape[0]

    # calculate f_wb for all examples.
    f_wb = X @ w + b
    # calculate cost
    total_cost = (1/(2*m)) * np.sum((f_wb-y)**2)

    if verbose: print("f_wb:")
    if verbose: print(f_wb)

    return total_cost

def compute_gradient_matrix(X, y, w, b):
    """
    Computes the gradient for linear regression

    Args:
      X (ndarray (m,n)): Data, m examples with n features
      y (ndarray (m,)) : target values
      w (ndarray (n,)) : model parameters  
      b (scalar)       : model parameter
    Returns
      dj_dw (ndarray (n,1)): The gradient of the cost w.r.t. the parameters w.
      dj_db (scalar):        The gradient of the cost w.r.t. the parameter b.

    """
    m,n = X.shape
    f_wb = X @ w + b
    e   = f_wb - y
    dj_dw  = (1/m) * (X.T @ e)
    dj_db  = (1/m) * np.sum(e)

    return dj_db,dj_dw


# Loop version of multi-variable compute_cost
def compute_cost(X, y, w, b):
    """
    compute cost
    Args:
      X (ndarray (m,n)): Data, m examples with n features
      y (ndarray (m,)) : target values
      w (ndarray (n,)) : model parameters  
      b (scalar)       : model parameter
    Returns
      cost (scalar)    : cost
    """
    m = X.shape[0]
    cost = 0.0
    for i in range(m):
        f_wb_i = np.dot(X[i],w) + b           #(n,)(n,)=scalar
        cost = cost + (f_wb_i - y[i])**2
    cost = cost/(2*m)
    return cost 

def compute_gradient(X, y, w, b):
    """
    Computes the gradient for linear regression
    Args:
      X (ndarray (m,n)): Data, m examples with n features
      y (ndarray (m,)) : target values
      w (ndarray (n,)) : model parameters  
      b (scalar)       : model parameter
    Returns
      dj_dw (ndarray Shape (n,)): The gradient of the cost w.r.t. the parameters w.
      dj_db (scalar):             The gradient of the cost w.r.t. the parameter b.
    """
    m,n = X.shape           #(number of examples, number of features)
    dj_dw = np.zeros((n,))
    dj_db = 0.

    for i in range(m):
        err = (np.dot(X[i], w) + b) - y[i]
        for j in range(n):
            dj_dw[j] = dj_dw[j] + err * X[i,j]
        dj_db = dj_db + err
    dj_dw = dj_dw/m
    dj_db = dj_db/m

    return dj_db,dj_dw