NDArray

Float NDArray library accelerated with Accelerate Framework

func testGradientDescent() {
    // y = 0.3*x^2 + 0.2*x + 0.1
    let start = Date()
    
    let xs = NDArray.linspace(low: -1, high: 1, count: 300)
    
    // data
    var ys = 0.3*xs*xs + 0.2*xs + 0.1
    ys += NDArray.normal(mu: 0, sigma: 0.03, shape: xs.shape)
    
    print("xs: \(xs.shape), ys: \(ys.shape)")
    
    // x^2, x^1, x^0
    let features = NDArray.stack([xs*xs, xs, NDArray.ones(xs.shape)], newAxis: -1)
    print("features: \(features.shape)")
    
    var theta = NDArray([1, 1, 1])
    
    let alpha: Float = 0.1
    
    for i in 0..<2000 {
        
        // calculate loss
        let distance = sum(theta * features, along: 1) - ys
        let loss = mean(distance**2, along: 0) / 2
        
        // Update parameters
        
        let grads = distance.reshaped([-1, 1]) * features
        let update = alpha * mean(grads, along: 0)
        theta -= update
        
        if i%100 == 0 {
            print("\nstep: \(i)")
            print("loss: \(loss.asScalar())")
            print("grads: \(grads.shape)")
            print("update: \(update)")
            print("theta: \(theta)")
        }
    }
    
    print("\nanswer")
    print("theta: \(theta)")
    let distance = sum(theta * features, along: 1) - ys
    let loss = mean(distance**2, along: 0) / 2
    print("loss: \(loss.asScalar())")
    print("elapsed time: \(Date().timeIntervalSince(start))sec")
    print("")
}

answer
theta: NDArray(shape: [3], data: [0.300824702, 0.199845955, 0.102966547], strides: [1], baseOffset: 0)
loss: 0.00041115
elapsed time: 0.166108965873718sec s

Examples

t-ae/ndarray

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