Logistic Regression Prediction

Logistic regression is a linear model for yes/no questions. For each sample it computes a probability and turns it into a class:

z = x₁×w₁ + … + xₙ×wₙ + bias
p = 1 / (1 + e^(-z))
class = 1 if p ≥ threshold, otherwise 0

Implement logistic_predict(samples, weights, bias, threshold).

Input

samples: One row of n features per sample.

weights: n weights.

bias: The bias.

threshold: The probability needed to predict class 1 (strictly between 0 and 1).

Output

Return the predicted class (0 or 1) of each sample, in order.

Constraints

1 ≤ samples ≤ 1,000, 1 ≤ n ≤ 50

-100 ≤ each feature, weight and bias ≤ 100

0 < threshold < 1

Examples

Example 1

Input:
samples = [
    [2.0, 1.0],
    [0.0, 0.0],
    [-1.0, -3.0]
]
weights = [1.5, -0.5]
bias = -1.0
threshold = 0.5

Expected output:
[1, 0, 0]

Explanation

Sample 1: z = 3 - 0.5 - 1 = 1.5, p ≈ 0.818 → 1. Sample 2: z = -1, p ≈ 0.269 → 0. Sample 3: z = -1.5 + 1.5 - 1 = -1, p ≈ 0.269 → 0. Result: [1, 0, 0].

Example 2

Input:
samples = [
    [1.0],
    [2.0],
    [3.0]
]
weights = [1.0]
bias = -2.0
threshold = 0.7

Expected output:
[0, 0, 1]

Explanation

The probabilities are σ(-1) ≈ 0.269, σ(0) = 0.5 and σ(1) ≈ 0.731. Only the last one reaches the 0.7 threshold, so the result is [0, 0, 1].

References

Run the sample tests to check your code without submitting.

Example 1 Not run

Input:

samples = [
    [2.0, 1.0],
    [0.0, 0.0],
    [-1.0, -3.0]
]
weights = [1.5, -0.5]
bias = -1.0
threshold = 0.5

Expected output:

[1, 0, 0]

Your output

Example 2 Not run

Input:

samples = [
    [1.0],
    [2.0],
    [3.0]
]
weights = [1.0]
bias = -2.0
threshold = 0.7

Expected output:

[0, 0, 1]

Your output

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