Problema
After a neuron adds up its weighted inputs, an activation function decides its output. Without one, a neural network could only learn straight lines.
Implement activate(name, values) and apply the named function to every value:
| name | formula |
|---|---|
"sigmoid" | 1 / (1 + e^(-x)) |
"relu" | max(0, x) |
"leaky_relu" | x if x > 0, otherwise 0.01 × x |
"tanh" | (e^x - e^(-x)) / (e^x + e^(-x)) |
Input
name: One of "sigmoid", "relu", "leaky_relu" or "tanh".
values: A list of numbers.
Output
Return a list with the activation applied to each value, in the same order.
Restricciones
1 ≤ length of values ≤ 1,000
-50 ≤ each value ≤ 50
Ejemplos
Entrada:
name = "sigmoid"
values = [0.0, 2.0, -2.0]
Salida esperada:
expected = [0.5, 0.880797, 0.119203]Entrada:
std::string name = "sigmoid";
std::vector<double> values = {0.0, 2.0, -2.0};
Salida esperada:
std::vector<double> expected = {0.5, 0.880797, 0.119203};Entrada:
string name = "sigmoid";
double[] values = new double[] {0.0, 2.0, -2.0};
Salida esperada:
double[] expected = new double[] {0.5, 0.880797, 0.119203};Explicación
sigmoid(0) = 1 / (1 + 1) = 0.5, sigmoid(2) ≈ 0.880797 and sigmoid(-2) ≈ 0.119203. Sigmoid squeezes any number into the range 0 to 1.
Entrada:
name = "relu"
values = [-1.5, 0.0, 3.0]
Salida esperada:
expected = [0.0, 0.0, 3.0]Entrada:
std::string name = "relu";
std::vector<double> values = {-1.5, 0.0, 3.0};
Salida esperada:
std::vector<double> expected = {0.0, 0.0, 3.0};Entrada:
string name = "relu";
double[] values = new double[] {-1.5, 0.0, 3.0};
Salida esperada:
double[] expected = new double[] {0.0, 0.0, 3.0};Explicación
ReLU keeps positive values and turns negative values into 0: [0, 0, 3].
Cómo abordarlo
Inténtalo primero por tu cuenta y luego abre las pistas una a una.
Pista 1
Each activation is applied independently to every number, so focus on one value at a time.
Pista 2
Use the formula matching the given name and build a new list from the results.
Pista 3
Be careful with negative inputs, especially for ReLU and the leaky slope on the nonpositive side.
Complejidad esperada
O(n) time, O(n) extra space
Referencias
Este reto está adaptado para CodeUtility. Sigue el enunciado y las restricciones indicadas arriba.