"""Useful functions."""

import numpy as _np


def polyfit(x, y, monomials, algorithm='lstsq'):
    """Implement Custom polyfit."""
    X = _np.zeros((len(x), len(monomials)))
    N = _np.zeros((len(x), len(monomials)))
    for i in range(X.shape[1]):
        X[:, i] = x
        N[:, i] = monomials[i]
    XN = X ** N
    y_ = _np.zeros((len(y), 1))
    y_[:, 0] = y
    XNt = _np.transpose(XN)
    b = _np.dot(XNt, y_)
    X = _np.dot(XNt, XN)

    if algorithm is 'lstsq':
        r = _np.linalg.lstsq(X, b)
        coeffs = r[0][:, 0]
    else:
        r = _np.linalg.solve(X, b)
        coeffs = r[:, 0]

    # finds maximum diff and its base value
    y_fitted = _np.dot(XN, coeffs)
    y_diff = abs(y_fitted - y_[:, 0])
    max_error = max(y_diff)
    idx = [i for i, value in enumerate(y_diff) if value == max_error]
    base_value = y_[idx[0], 0]

    return (coeffs, (max_error, base_value))

"""Useful functions."""

import numpy as _np


def polyfit(x, y, monomials):
    """Implement Custom polyfit."""
    coef = _np.polynomial.polynomial.polyfit(x, y, deg=monomials)

    # finds maximum diff and its base value
    y_fitted = _np.polynomial.polynomial.polyval(x, coef)
    y_diff = abs(y_fitted - y)
    idx = _np.argmax(y_diff)

    coeffs = coef[monomials]
    return (coeffs, (y_diff[idx], y[idx]))

Change implementaton of polyfit method.
API: Change implementaton of polyfit method.

Use new numpy.polynomial.polynomial.polyfit instead of implementing leastsquares by hand. This method is supposed to be more robust to numerical errors.
With this change, the keyword argument algorithm was removed.

  """Useful functions."""
  
  import numpy as _np
  
  
- def polyfit(x, y, monomials, algorithm='lstsq'):
+ def polyfit(x, y, monomials):
      """Implement Custom polyfit."""
+     coef = _np.polynomial.polynomial.polyfit(x, y, deg=monomials)
-     X = _np.zeros((len(x), len(monomials)))
-     N = _np.zeros((len(x), len(monomials)))
-     for i in range(X.shape[1]):
-         X[:, i] = x
-         N[:, i] = monomials[i]
-     XN = X ** N
-     y_ = _np.zeros((len(y), 1))
-     y_[:, 0] = y
-     XNt = _np.transpose(XN)
-     b = _np.dot(XNt, y_)
-     X = _np.dot(XNt, XN)
- 
-     if algorithm is 'lstsq':
-         r = _np.linalg.lstsq(X, b)
-         coeffs = r[0][:, 0]
-     else:
-         r = _np.linalg.solve(X, b)
-         coeffs = r[:, 0]
  
      # finds maximum diff and its base value
-     y_fitted = _np.dot(XN, coeffs)
+     y_fitted = _np.polynomial.polynomial.polyval(x, coef)
-     y_diff = abs(y_fitted - y_[:, 0])
+     y_diff = abs(y_fitted - y)
+     idx = _np.argmax(y_diff)
-     max_error = max(y_diff)
-     idx = [i for i, value in enumerate(y_diff) if value == max_error]
-     base_value = y_[idx[0], 0]
  
-     return (coeffs, (max_error, base_value))
+     coeffs = coef[monomials]
+     return (coeffs, (y_diff[idx], y[idx]))
  
Change implementaton of polyfit method.
## Code Before:
"""Useful functions."""

import numpy as _np


def polyfit(x, y, monomials, algorithm='lstsq'):
    """Implement Custom polyfit."""
    X = _np.zeros((len(x), len(monomials)))
    N = _np.zeros((len(x), len(monomials)))
    for i in range(X.shape[1]):
        X[:, i] = x
        N[:, i] = monomials[i]
    XN = X ** N
    y_ = _np.zeros((len(y), 1))
    y_[:, 0] = y
    XNt = _np.transpose(XN)
    b = _np.dot(XNt, y_)
    X = _np.dot(XNt, XN)

    if algorithm is 'lstsq':
        r = _np.linalg.lstsq(X, b)
        coeffs = r[0][:, 0]
    else:
        r = _np.linalg.solve(X, b)
        coeffs = r[:, 0]

    # finds maximum diff and its base value
    y_fitted = _np.dot(XN, coeffs)
    y_diff = abs(y_fitted - y_[:, 0])
    max_error = max(y_diff)
    idx = [i for i, value in enumerate(y_diff) if value == max_error]
    base_value = y_[idx[0], 0]

    return (coeffs, (max_error, base_value))

## Instruction:
Change implementaton of polyfit method.
## Code After:
"""Useful functions."""

import numpy as _np


def polyfit(x, y, monomials):
    """Implement Custom polyfit."""
    coef = _np.polynomial.polynomial.polyfit(x, y, deg=monomials)

    # finds maximum diff and its base value
    y_fitted = _np.polynomial.polynomial.polyval(x, coef)
    y_diff = abs(y_fitted - y)
    idx = _np.argmax(y_diff)

    coeffs = coef[monomials]
    return (coeffs, (y_diff[idx], y[idx]))
