Examples missing a feature (`theta = n` but should be `theta = n + 1`)

[Antoine-lb]

• Day: 07
• Exercise: 05

Hello 👋

The example is using a len(theta) = n but should be len(theta) = n + 1 because after intercepting x it with a column of 1s the two matrices are incompatible.

The current example:

x = np.array([
        [ -6,  -7,  -9],
        [ 13,  -2,  14],
        [ -7,  14,  -1],
        [ -8,  -4,   6],
        [ -5, -9, 6],
        [ 1, -5, 11],
        [ 9,-11, 8]])

theta1 = np.array([3, 0.5, -6]) # missing a value (feature) here

What it should look like:

x = np.array(...)

theta1 = np.array([0, 3, 0.5, -6]) # adding a 0 (or any value) as the first feature would solve the problem 

Note: the two examples have the same problem

Disclaimer: I'm new to this so maybe I got something wrong

[Antoine-lb]

I can do a pull request myself if you want to

[madvid]

No need, I have to update all the days, there are multiple corrections already made in another git repo, I just have to do it on dev and do a merge request

[Antoine-lb]

ok, I'm doing the bootcamp right now so I have many errors written down on the side, hit me up if you want some help

[madvid]

If you are still willing to help, could you do a check list in an issue with all the erorrs you encountered ? Let me know if you are doing it, i'll will do the update of the subjects today.

[madvid]

@Antoine-lb , tu as la liste des erreurs sous la main ou pas ? (si non ce n'est pas grave, s'il y a des oublis de ma part les gens feront des issues)

[Antoine-lb]

• Module07/ex05 = The one is this Issue
• Module07/ex06 = declared variables and used variables are different in the examples (for example x is then called X2) so the example does not even compile
• Module08/page 22/second paragraph/ second line = "but mess they up the content" should be "but they mess up the content" I believe

Sadly I stoped writing them down after your initial comment. For how long are you open for changes? we are at Agritech so many are doing the piscine, I can take care of sending errors or even pull request them with fixes if you want to

[Antoine-lb]

Not sure if is an error but I think you should check it:

• Module06/ex03 = Many in here doing the piscine where not able to get the same results as the example.

Expected:

[21.0342574, 587.36875564]
[58.86823748, 2229.72297889]

What we got:

[-19.034257402000005, -586.668755635948]
[-57.868237476000004, -2230.1229788875144]

Many in here had the same result, maybe we are wrong but we ended up moving forward after not finding what was wrong.

Here is my code:

def cost_(y, y_hat):
    """Computes the half mean squared error of two non-empty numpy.ndarray, without any for 􏰀→ loop. The two arrays must have the same dimensions.
    Args:
      y: has to be an numpy.ndarray, a vector.
      y_hat: has to be an numpy.ndarray, a vector.
    Returns:
      The half mean squared error of the two vectors as a float.
      None if y or y_hat are empty numpy.ndarray.
      None if y and y_hat does not share the same dimensions.
    Raises:
      This function should not raise any Exceptions.
    """
    return (1 / (2 * y.shape[0])) * np.dot(y_hat - y, y_hat - y)

def add_intercept(x):
    """Adds a column of 1's to the non-empty numpy.ndarray x.
    Args:
      x: has to be an numpy.ndarray, a vector of dimension m * 1.
    Returns:
      X as a numpy.ndarray, a vector of dimension m * 2.
      None if x is not a numpy.ndarray.
      None if x is a empty numpy.ndarray.
    Raises:
      This function should not raise any Exception.
    """
    a1 = x
    if (len(x.shape) == 1):
        a1 = x.reshape(x.shape[0], 1)
    an_array = np.insert(a1, 0, [[1]], axis=1)
    return an_array

def predict_(x, theta):
    """Computes the vector of prediction y_hat from two non-empty numpy.ndarray.
    Args:
      x: has to be an numpy.ndarray, a vector of dimension m * 1.
      theta: has to be an numpy.ndarray, a vector of dimension 2 * 1.
    Returns:
      y_hat as a numpy.ndarray, a vector of dimension m * 1.
      None if x or theta are empty numpy.ndarray.
      None if x or theta dimensions are not appropriate.
    Raises:
      This function should not raise any Exceptions.
    """
    intercepted_x = add_intercept(x)
    return (np.dot(intercepted_x, theta))

def simple_gradient(x, y, theta):
    """Computes a gradient vector from three non-empty numpy.ndarray, without any for-loop. 􏰀→ The three arrays must have compatible dimensions.
    Args:
      x: has to be an numpy.ndarray, a vector of dimension m * 1.
      y: has to be an numpy.ndarray, a vector of dimension m * 1.
      theta: has to be an numpy.ndarray, a 2 * 1 vector.
    Returns:
        The gradient as a numpy.ndarray, a vector of dimension 2 * 1.
        None if x, y, or theta are empty numpy.ndarray.
        None if x, y and theta do not have compatible dimensions.
    Raises:
        This function should not raise any Exception.
    """
    res = [0, 0]
    res[0] = 1 / len(x) * np.sum(predict_(x, theta) - y)
    res[1] = 1 / len(x) * np.sum((predict_(x, theta) - y) * x)
    return res

[madvid]

Just open issue if you find other mistakes, or if you wish to do a pull request you have to create a new branch and pull request on dev when you have correct the mistakes it and regenerate the corresponding pdf. So it is better if I do this I think

[madvid]

For the exercise on simple gradient, the errors in the examples was already fixed have been corrected in the dev branch. It will be very soon on master

[madvid]

For:

Module07/ex06 = declared variables and used variables are different in the examples (for example x is then called X2) so the example does not even compile

well spotted, there were no issue reporting this error. Hopefully python is not a compiled language but an interpreted one ;)

[madvid]

Thank you for the report about the issues, there are fixed in the branch (correction_before_session_2021). Pdf will be regenerate and then merge request will follow on dev and next on main