Python optimize_discrete Examples

Example #1

img = cam.getImage().scale(400, 400)
img2 = img.binarize()


def costImage(i):
    # Make image black and white
    img1 = img.binarize(int(i))
    mat = img1.getNumpy()
    countW = np.count_nonzero(mat)
    countB = mat.size - countW
    return ((countW - countB) / float(mat.size))**2


params = {}  #bayesopt.initialize_params()
params['n_iterations'] = 15
params['n_init_samples'] = 5

valid_values = np.transpose(np.array(range(256), dtype=float, ndmin=2))
mvalue, x_out, error = bayesopt.optimize_discrete(costImage, valid_values,
                                                  params)

x_out = int(x_out)
print x_out
img1 = img.binarize(x_out)

img1 = img.sideBySide(img1).sideBySide(img2)
img1.drawText("Threshold: " + str(x_out))
img1.show()

foo = raw_input('Press any key')

Example #2

start = clock()

mvalue, x_out, error = bayesopt.optimize(testfunc, n, lb, ub, params)

print "Result", x_out
print "Seconds", clock() - start

print "OO implementation"
bo_test = BayesOptTest(n)
bo_test.parameters = params
bo_test.lower_bound = lb
bo_test.upper_bound = ub

start = clock()
mvalue, x_out, error = bo_test.optimize()

print "Result", x_out
print "Seconds", clock() - start

print "Callback discrete implementation"
x_set = np.random.rand(100, n)
start = clock()

mvalue, x_out, error = bayesopt.optimize_discrete(testfunc, x_set, params)

print "Result", x_out
print "Seconds", clock() - start

value = np.array([testfunc(i) for i in x_set])
print "Optimun", x_set[value.argmin()]

Example #3

    def optimize(self):
        min_val, x_out, error = bo.optimize_discrete(self.evaluateSample,
                                                     self.x_set, self.params)

        return min_val, x_out, error

Example #4

File: demo_distance.py Project: mathkann/bayesopt

mvalue, x_out, error = bayesopt.optimize(testfunc, n, lb, ub, params)

print "Result", x_out
print "Seconds", clock() - start


print "OO implementation"
bo_test = BayesOptTest(n)
bo_test.parameters = params
bo_test.lower_bound = lb
bo_test.upper_bound = ub

start = clock()
mvalue, x_out, error = bo_test.optimize()

print "Result", x_out
print "Seconds", clock() - start


print "Callback discrete implementation"
x_set = np.random.rand(100,n)
start = clock()

mvalue, x_out, error = bayesopt.optimize_discrete(testfunc, x_set, params)

print "Result", x_out
print "Seconds", clock() - start

value = np.array([testfunc(i) for i in x_set])
print "Optimun", x_set[value.argmin()]

Example #5

File: demo_cam.py Project: rmcantin/bayesopt

#Load images
img = cam.getImage().scale(400,400)
img2 = img.binarize()


def costImage(i):
    # Make image black and white
    img1 = img.binarize(int(i))
    mat = img1.getNumpy()
    countW = np.count_nonzero(mat);
    countB = mat.size-countW
    return ((countW-countB)/float(mat.size))**2

params = {} #bayesopt.initialize_params()
params['n_iterations'] = 15
params['n_init_samples'] = 5

valid_values = np.transpose(np.array(range(256), dtype=float, ndmin=2))
mvalue, x_out, error = bayesopt.optimize_discrete(costImage,
                                                  valid_values, params)

x_out = int(x_out)
print(x_out)
img1 = img.binarize(x_out)

img1 = img.sideBySide(img1).sideBySide(img2)
img1.drawText("Threshold: "+str(x_out))
img1.show()

foo = raw_input('Press any key')

Example #6

File: bayesoptmodule.py Project: rmcantin/bayesopt

 def optimize(self):
     min_val, x_out, error = bo.optimize_discrete(self.evaluateSample,
                                                 self.x_set,
                                                 self.params)
     
     return min_val, x_out, error