diff options
Diffstat (limited to 'src')
-rw-r--r-- | src/monkey_brain/main.zig | 69 |
1 files changed, 44 insertions, 25 deletions
diff --git a/src/monkey_brain/main.zig b/src/monkey_brain/main.zig index 52ff9e5..5d0e616 100644 --- a/src/monkey_brain/main.zig +++ b/src/monkey_brain/main.zig @@ -1,13 +1,14 @@ const std = @import("std"); const testing = std.testing; +const math = std.math; const input_size: usize = 2; const training_set_size: usize = 4; const learning_rate: f64 = 0.1; -const epochs: u64 = 100 * 1000; +const epochs: u64 = 1000000; fn sigmoid(x: f64) f64 { - return 1.0 / (1.0 + std.math.exp(-x)); + return 1.0 / (1.0 + math.exp(-x)); } fn sigmoid_derivative(output: f64) f64 { @@ -15,23 +16,21 @@ fn sigmoid_derivative(output: f64) f64 { } fn predict(weights: [input_size]f64, bias: f64, inputs: [input_size]f64) f64 { - var total: f64 = 0.0; - for (0..input_size) |i| { - total += weights[i] * inputs[i]; + var total: f64 = bias; + for (inputs, 0..) |input, i| { + total += weights[i] * input; } - total += bias; return sigmoid(total); } fn train(weights: *[input_size]f64, bias: *f64, training_data: [training_set_size][input_size]f64, labels: [training_set_size]f64) void { for (0..epochs) |_| { - for (0..training_set_size) |i| { - const prediction = predict(weights.*, bias.*, training_data[i]); - const err = labels[i] - prediction; + for (training_data, labels) |inputs, label| { + const prediction = predict(weights.*, bias.*, inputs); + const err = label - prediction; const adjustment = err * sigmoid_derivative(prediction); - - for (0..input_size) |j| { - weights[j] += learning_rate * adjustment * training_data[i][j]; + for (inputs, 0..) |input, j| { + weights[j] += learning_rate * adjustment * input; } bias.* += learning_rate * adjustment; } @@ -39,24 +38,44 @@ fn train(weights: *[input_size]f64, bias: *f64, training_data: [training_set_siz } pub fn main() !void { - const w1 = std.crypto.random.float(f64); - const w2 = std.crypto.random.float(f64); - - var weights: [input_size]f64 = .{ w1, w2 }; - var bias: f64 = 0.0; - - const training_data: [training_set_size][input_size]f64 = .{ .{ 0, 0 }, .{ 0, 1 }, .{ 1, 0 }, .{ 1, 1 } }; + var weights = [_]f64{ std.crypto.random.float(f64), std.crypto.random.float(f64) }; + var bias: f64 = std.crypto.random.float(f64); - const labels: [training_set_size]f64 = .{ 0, 0, 0, 1 }; + const training_data = [_][input_size]f64{ + .{ 0, 0 }, + .{ 0, 1 }, + .{ 1, 0 }, + .{ 1, 1 }, + }; + const labels = [_]f64{ 0, 1, 1, 1 }; // OR operation train(&weights, &bias, training_data, labels); - for (0..training_set_size) |i| { - const prediction = predict(weights, bias, training_data[i]); - std.log.info("Input {} {}, Predicted output: {}", .{ training_data[i][0], training_data[i][1], prediction }); + std.debug.print("Trained weights: {d}, {d}\n", .{ weights[0], weights[1] }); + std.debug.print("Trained bias: {d}\n", .{bias}); + + for (training_data, labels) |inputs, expected| { + const prediction = predict(weights, bias, inputs); + std.debug.print("Input: {d}, {d}, Predicted: {d:.4}, Expected: {d}\n", .{ inputs[0], inputs[1], prediction, expected }); } } -test "hello" { - try testing.expect(true); +test "OR gate" { + var weights = [_]f64{ 0, 0 }; + var bias: f64 = 0; + + const training_data = [_][input_size]f64{ + .{ 0, 0 }, + .{ 0, 1 }, + .{ 1, 0 }, + .{ 1, 1 }, + }; + const labels = [_]f64{ 0, 1, 1, 1 }; + + train(&weights, &bias, training_data, labels); + + for (training_data, labels) |inputs, expected| { + const prediction = predict(weights, bias, inputs); + try testing.expect((prediction - expected) < 0.1); + } } |