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const std = @import("std");
const math = std.math;
const Random = std.Random;
const ArrayList = std.ArrayList;
const testing = std.testing;
pub const Neuron = struct {
weights: ArrayList(f64),
bias: f64,
pub fn init(allocator: std.mem.Allocator, num_inputs: usize) !Neuron {
var weights = ArrayList(f64).init(allocator);
var prng = Random.DefaultPrng.init(blk: {
const seed: u64 = @intCast(std.time.milliTimestamp());
break :blk seed;
});
var random = prng.random();
var i: usize = 0;
while (i < num_inputs) : (i += 1) {
try weights.append(random.float(f64) * 2 - 1);
}
return Neuron{
.weights = weights,
.bias = random.float(f64) * 2 - 1,
};
}
pub fn deinit(self: *Neuron) void {
self.weights.deinit();
}
pub fn activate(self: *const Neuron, inputs: []const f64) f64 {
var sum: f64 = self.bias;
for (self.weights.items, 0..) |weight, i| {
sum += inputs[i] * weight;
}
return sigmoid(sum);
}
};
fn sigmoid(x: f64) f64 {
return 1.0 / (1.0 + math.exp(-x));
}
test "Neuron initialization" {
const allocator = testing.allocator;
var neuron = try Neuron.init(allocator, 2);
defer neuron.deinit();
std.debug.print("Testing neuron", .{});
try testing.expect(neuron.weights.items.len == 2);
try testing.expect(neuron.bias >= -1 and neuron.bias <= 1);
}
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