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Added doctest, docstring and typehint for sigmoid_function & cost_function #10828

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merged 8 commits into from
Oct 26, 2023
Merged

Added doctest, docstring and typehint for sigmoid_function & cost_function #10828

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9ceb099
Added doctest for sigmoid_function & cost_function
Suyashd999 Oct 23, 2023
fe3cb40
Merge branch 'TheAlgorithms:master' into master
Suyashd999 Oct 25, 2023
d1bc2e8
Update logistic_regression.py
Suyashd999 Oct 25, 2023
79d3d54
Update logistic_regression.py
Suyashd999 Oct 25, 2023
694bb03
Minor formatting changes in doctests
tianyizheng02 Oct 26, 2023
59dcc44
Apply suggestions from code review
tianyizheng02 Oct 26, 2023
4ef1ec8
Made requested changes in logistic_regression.py
Suyashd999 Oct 26, 2023
c9e403c
Apply suggestions from code review
tianyizheng02 Oct 26, 2023
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Made requested changes in logistic_regression.py
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@Suyashd999
Suyashd999 authored Oct 26, 2023
commit 4ef1ec81e76a912104af8600fed03b668ce27a35
35 changes: 15 additions & 20 deletions machine_learning/logistic_regression.py
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Expand Up @@ -27,7 +27,7 @@
# classification problems


def sigmoid_function(z: np.ndarray) -> np.ndarray:
def sigmoid_function(z: float | np.ndarray) -> float | np.ndarray:
"""
Also known as Logistic Function.

Expand All @@ -46,23 +46,23 @@ def sigmoid_function(z: np.ndarray) -> np.ndarray:
Examples:
>>> sigmoid_function(4)
0.9820137900379085
>>> sigmoid_function(np.array([-3, 3]))
>>> sigmoid_function(np.array([-3,3]))
array([0.04742587, 0.95257413])
>>> sigmoid_function(np.array([-3, 3, 1]))
>>> sigmoid_function(np.array([-3,3,1]))
array([0.04742587, 0.95257413, 0.73105858])
>>> sigmoid_function(np.array([-0.01, -2, -1.9]))
>>> sigmoid_function(np.array([-0.01,-2,-1.9]))
array([0.49750002, 0.11920292, 0.13010847])
>>> sigmoid_function(np.array([-1.3, 5.3, 12]))
>>> sigmoid_function(np.array([-1.3,5.3,12]))
array([0.21416502, 0.9950332 , 0.99999386])
>>> sigmoid_function(np.array([0.01, 0.02, 4.1]))
>>> sigmoid_function(np.array([0.01,0.02,4.1]))
array([0.50249998, 0.50499983, 0.9836975 ])
>>> sigmoid_function(np.array([0.8]))
array([0.68997448])
"""
return 1 / (1 + np.exp(-z))


def cost_function(h: np.ndarray, y: np.ndarray) -> int:
def cost_function(h: np.ndarray, y: np.ndarray) -> float:
"""
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Cost function quantifies the error between predicted and expected values.
The cost function used in Logistic Regression is called Log Loss
Expand All @@ -83,22 +83,17 @@ def cost_function(h: np.ndarray, y: np.ndarray) -> int:
@param y: the actual binary label associated with input example 'x'

Examples:
>>> estimations = np.array([
... sigmoid_function(0.3), sigmoid_function(-4.3), sigmoid_function(8.1)
... ])
>>> cost_function(h=estimations, y=np.array([1, 0, 1]))
>>> estimations = sigmoid_function(np.array([0.3,-4.3,8.1]))
>>> cost_function(h=estimations,y=np.array([1,0,1]))
0.18937868932131605
>>> estimations = np.array([
... sigmoid_function(4), sigmoid_function(3), sigmoid_function(1)
... ])
>>> cost_function(h=estimations, y=np.array([1, 0, 0]))
>>> estimations = sigmoid_function(np.array([4,3,1]))
>>> cost_function(h=estimations,y=np.array([1,0,0]))
1.459999655669926
>>> estimations = np.array([
... sigmoid_function(4), sigmoid_function(-3), sigmoid_function(-1)
... ])
>>> cost_function(h=estimations, y=np.array([1, 0, 0]))
>>> estimations = sigmoid_function(np.array([4,-3,-1]))
>>> cost_function(h=estimations,y=np.array([1,0,0]))
0.1266663223365915
>>> cost_function(h=np.array([sigmoid_function(0)]), y=np.array([1]))
>>> estimations = sigmoid_function(0)
>>> cost_function(h=estimations,y=np.array([1]))
0.6931471805599453

References:
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