Problem
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.
Constraints
1 ≤ length of values ≤ 1,000
-50 ≤ each value ≤ 50
Examples
Input:
name = "sigmoid"
values = [0.0, 2.0, -2.0]
Expected output:
expected = [0.5, 0.880797, 0.119203]Input:
std::string name = "sigmoid";
std::vector<double> values = {0.0, 2.0, -2.0};
Expected output:
std::vector<double> expected = {0.5, 0.880797, 0.119203};Input:
string name = "sigmoid";
double[] values = new double[] {0.0, 2.0, -2.0};
Expected output:
double[] expected = new double[] {0.5, 0.880797, 0.119203};Explanation
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.
Input:
name = "relu"
values = [-1.5, 0.0, 3.0]
Expected output:
expected = [0.0, 0.0, 3.0]Input:
std::string name = "relu";
std::vector<double> values = {-1.5, 0.0, 3.0};
Expected output:
std::vector<double> expected = {0.0, 0.0, 3.0};Input:
string name = "relu";
double[] values = new double[] {-1.5, 0.0, 3.0};
Expected output:
double[] expected = new double[] {0.0, 0.0, 3.0};Explanation
ReLU keeps positive values and turns negative values into 0: [0, 0, 3].
How to approach it
Try on your own first, then open one hint at a time.
Hint 1
Each activation is applied independently to every number, so focus on one value at a time.
Hint 2
Use the formula matching the given name and build a new list from the results.
Hint 3
Be careful with negative inputs, especially for ReLU and the leaky slope on the nonpositive side.
Expected complexity
O(n) time, O(n) extra space
References
This challenge is adapted for CodeUtility. Follow the statement and constraints above.