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How to use test_projection method in Airtest

Best Python code snippet using Airtest

hill_climbing.py

Source:hill_climbing.py

...48    return best_theta,  best_ratio, best_w, best_h, best_l49@jit(nopython=True)50def hill_climb(p2, p2_inv, box_2d, x2d, y2d, z2d, w3d, h3d, l3d, ry3d, step_r_init, r_lim=0.0,min_ol_dif=0.0):51    step_r = step_r_init52    ol_best = test_projection(p2, p2_inv, box_2d, x2d, y2d, z2d, w3d, h3d, l3d, ry3d)53    # attempt to fit z/rot more properly54    while (step_r > r_lim):55        if step_r > r_lim:56            ol_neg = test_projection(p2, p2_inv, box_2d, x2d, y2d, z2d, w3d, h3d, l3d, ry3d - step_r)57            ol_pos = test_projection(p2, p2_inv, box_2d, x2d, y2d, z2d, w3d, h3d, l3d, ry3d + step_r)58            59            invalid = ((ol_pos - ol_best) <= min_ol_dif) and ((ol_neg - ol_best) <= min_ol_dif)60            if invalid:61                step_r = step_r * 0.562            elif (ol_pos - ol_best) > min_ol_dif and ol_pos > ol_neg:63                ry3d += step_r64                ol_best = ol_pos65            elif (ol_neg - ol_best) > min_ol_dif:66                ry3d -= step_r67                ol_best = ol_neg68            else:69                step_r = step_r * 0.570    while ry3d > 3.14: ry3d -= 3.14 * 271    while ry3d < (-3.14): ry3d += np.pi * 272    return ry3d, ol_best73@jit(nopython=True)74def test_projection(p2, p2_inv, box_2d, cx, cy, z, w3d, h3d, l3d, rotY):75    x = box_2d[0]76    y = box_2d[1]77    x2 = box_2d[2]78    y2 = box_2d[3]79    coord3d = p2_inv.dot(np.array([cx * z, cy * z, z, 1]))80    cx3d = coord3d[0]81    cy3d = coord3d[1]82    cz3d = coord3d[2]83    #top_3d = p2_inv.dot(np.array([cx * z, (cy-h3d/2) * z, z, 1]))84    # put back on ground first85    #cy3d += h3d/286    fy = p2[1, 1]87    # re-compute the 2D box using 3D (finally, avoids clipped boxes)88    verts3d, corners_3d = project_3d(p2, cx3d, cy3d, cz3d, w3d, h3d, l3d, rotY)...

test_projection.py

Source:test_projection.py

1"""2.. module:: test_projection3  :synopsis: Test Projection strategy4.. moduleauthor:: David Eriksson <dme65@cornell.edu>5"""6from pySOT import check_opt_prob, SyncStrategyProjection, \7    LatinHypercube, RBFInterpolant, CubicKernel, LinearTail, \8    CandidateDYCORS9from poap.controller import ThreadController, BasicWorkerThread10import numpy as np11import os.path12import logging13class AckleyUnit:14    def __init__(self, dim=10):15        self.xlow = -1 * np.ones(dim)16        self.xup = 1 * np.ones(dim)17        self.dim = dim18        self.info = str(dim)+"-dimensional Ackley function on the unit sphere \n" +\19                             "Global optimum: f(1,0,...,0) = ... = f(0,0,...,1) = " +\20                             str(np.round(20*(1-np.exp(-0.2/np.sqrt(dim))), 3))21        self.min = 20*(1 - np.exp(-0.2/np.sqrt(dim)))22        self.integer = []23        self.continuous = np.arange(0, dim)24        check_opt_prob(self)25    def objfunction(self, x):26        n = float(len(x))27        return -20.0 * np.exp(-0.2*np.sqrt(np.sum(x**2)/n)) - \28            np.exp(np.sum(np.cos(2.0*np.pi*x))/n) + 20 + np.exp(1)29    def eval_eq_constraints(self, x):30        return np.linalg.norm(x) - 131def main():32    if not os.path.exists("./logfiles"):33        os.makedirs("logfiles")34    if os.path.exists("./logfiles/test_projection.log"):35        os.remove("./logfiles/test_projection.log")36    logging.basicConfig(filename="./logfiles/test_projection.log",37                        level=logging.INFO)38    print("\nNumber of threads: 4")39    print("Maximum number of evaluations: 1000")40    print("Sampling method: CandidateDYCORS")41    print("Experimental design: Latin Hypercube")42    print("Surrogate: Cubic RBF")43    nthreads = 444    maxeval = 100045    nsamples = nthreads46    data = AckleyUnit(dim=10)47    print(data.info)48    def projection(x):49        return x / np.linalg.norm(x)50    # Create a strategy and a controller51    controller = ThreadController()52    controller.strategy = \53        SyncStrategyProjection(54            worker_id=0, data=data,55            maxeval=maxeval, nsamples=nsamples,56            exp_design=LatinHypercube(dim=data.dim, npts=2*(data.dim+1)),57            response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail, maxp=maxeval),58            sampling_method=CandidateDYCORS(data=data, numcand=100*data.dim),59            proj_fun=projection60        )61    # Launch the threads and give them access to the objective function62    for _ in range(nthreads):63        worker = BasicWorkerThread(controller, data.objfunction)64        controller.launch_worker(worker)65    # Run the optimization strategy66    result = controller.run()67    print('Best value found: {0}'.format(result.value))68    print('Best solution found: {0}'.format(69        np.array_str(result.params[0], max_line_width=np.inf,70                     precision=5, suppress_small=True)))71    print('||x||_2 = {0}\n'.format(np.linalg.norm(result.params[0])))72if __name__ == '__main__':...

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