문제
An artificial neuron multiplies each input by a weight, adds a bias, and passes the result through an activation function:
z = x₁×w₁ + x₂×w₂ + … + xₙ×wₙ + bias
output = sigmoid(z) = 1 / (1 + e^(-z))
Implement neuron_output(inputs, weights, bias).
Input
inputs: The input values.
weights: One weight per input (same length as inputs).
bias: The neuron's bias.
Output
Return the neuron's output, a decimal number between 0 and 1.
제약 조건
1 ≤ length of inputs = length of weights ≤ 100
-10 ≤ each input, weight and bias ≤ 10
예시
입력:
bias = 0.5
inputs = [1.0, 2.0, 3.0]
weights = [0.2, 0.4, 0.6]
예상 출력:
expected = 0.964429입력:
double bias = 0.5;
std::vector<double> inputs = {1.0, 2.0, 3.0};
std::vector<double> weights = {0.2, 0.4, 0.6};
예상 출력:
double expected = 0.964429;입력:
double bias = 0.5;
double[] inputs = new double[] {1.0, 2.0, 3.0};
double[] weights = new double[] {0.2, 0.4, 0.6};
예상 출력:
double expected = 0.964429;설명
z = 1×0.2 + 2×0.4 + 3×0.6 + 0.5 = 3.3, and sigmoid(3.3) ≈ 0.964429.
입력:
bias = 0.0
inputs = [0.5, -1.0]
weights = [2.0, 1.0]
예상 출력:
expected = 0.5입력:
double bias = 0.0;
std::vector<double> inputs = {0.5, -1.0};
std::vector<double> weights = {2.0, 1.0};
예상 출력:
double expected = 0.5;입력:
double bias = 0.0;
double[] inputs = new double[] {0.5, -1.0};
double[] weights = new double[] {2.0, 1.0};
예상 출력:
double expected = 0.5;설명
z = 0.5×2 + (-1)×1 + 0 = 0, and sigmoid(0) = 0.5: the neuron is exactly undecided.
접근 방법
먼저 스스로 풀어 본 뒤 힌트를 하나씩 열어 보세요.
힌트 1
This is just a weighted sum of the inputs plus a bias, followed by one sigmoid.
힌트 2
Pair each input with its corresponding weight and accumulate the products before adding the bias.
힌트 3
Watch that the final output is passed through sigmoid, not returned as the raw sum.
예상 복잡도
O(n) time, O(1) extra space
참고 자료
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