def setUp(self): """ Set up """ self.kernel = asam.maxima_mean_kernel(np.vstack([self.Q_ref, self.Q_ref+.5]), self.rho_D)
def setUp(self): """ Set up """ self.kernel = asam.maxima_mean_kernel( np.vstack([self.Q_ref, self.Q_ref + .5]), self.rho_D)
interp_values[:, i] = griddata(points.transpose(), Q[:, i], inputs) return interp_values # Create kernel maximum = 1/np.product(bin_size) def rho_D(outputs): rho_left = np.repeat([Q_ref-.5*bin_size], outputs.shape[0], 0) rho_right = np.repeat([Q_ref+.5*bin_size], outputs.shape[0], 0) rho_left = np.all(np.greater_equal(outputs, rho_left), axis=1) rho_right = np.all(np.less_equal(outputs, rho_right), axis=1) inside = np.logical_and(rho_left, rho_right) max_values = np.repeat(maximum, outputs.shape[0], 0) return inside.astype('float64')*max_values kernel_mm = asam.maxima_mean_kernel(np.array([Q_ref]), rho_D) kernel_rD = asam.rhoD_kernel(maximum, rho_D) kernel_m = asam.maxima_kernel(np.array([Q_ref]), rho_D) heur_list = [kernel_mm, kernel_rD, kernel_m] # Create sampler chain_length = 125 num_chains = 80 num_samples = num_chains*chain_length sampler = asam.sampler(num_samples, chain_length, model) inital_sample_type = "lhs" # Get samples # Run with varying kernels gen_results = sampler.run_gen(heur_list, rho_D, maximum, param_domain, transition_set, sample_save_file)
# Create kernel maximum = 1 / np.product(bin_size) def rho_D(outputs): rho_left = np.repeat([Q_ref - .5 * bin_size], outputs.shape[0], 0) rho_right = np.repeat([Q_ref + .5 * bin_size], outputs.shape[0], 0) rho_left = np.all(np.greater_equal(outputs, rho_left), axis=1) rho_right = np.all(np.less_equal(outputs, rho_right), axis=1) inside = np.logical_and(rho_left, rho_right) max_values = np.repeat(maximum, outputs.shape[0], 0) return inside.astype('float64') * max_values kernel_mm = asam.maxima_mean_kernel(np.array([Q_ref]), rho_D) kernel_rD = asam.rhoD_kernel(maximum, rho_D) kernel_m = asam.maxima_kernel(np.array([Q_ref]), rho_D) heur_list = [kernel_mm, kernel_rD, kernel_m] # Create sampler chain_length = 125 num_chains = 80 num_samples = num_chains * chain_length sampler = asam.sampler(num_samples, chain_length, model) inital_sample_type = "lhs" # Get samples # Run with varying kernels gen_results = sampler.run_gen(heur_list, rho_D, maximum, param_domain, transition_set, sample_save_file)