def test_compare_yield(): """ Tests :meth:`bet.postProcess.postTools.compare_yield` without column headings """ sample_quality = np.random.random((10,)) sort_ind = np.argsort(sample_quality) run_param = [] for i in range(10): run_param.append(np.random.random((4,))) try: postTools.compare_yield(sort_ind, sample_quality, run_param) go = True except (RuntimeError, TypeError, NameError): go = False nptest.assert_equal(go, True)
def test_compare_yield(): """ Tests :meth:`bet.postProcess.postTools.compare_yield` without column headings """ sample_quality = np.random.random((10, )) sort_ind = np.argsort(sample_quality) run_param = [] for i in range(10): run_param.append(np.random.random((4, ))) try: postTools.compare_yield(sort_ind, sample_quality, run_param) go = True except (RuntimeError, TypeError, NameError): go = False nptest.assert_equal(go, True)
def test_compare_yield_CH(): """ Tests :meth:`bet.postProcess.postTools.compare_yield` with column headings """ sample_quality = np.random.random((10,)) sort_ind = np.argsort(sample_quality) run_param = [] for i in range(10): run_param.append(np.random.random((4,))) column_headings = ['swallow', 'coconut', 'ni', 'shrubbery'] try: postTools.compare_yield(sort_ind, sample_quality, run_param, column_headings) go = True except (RuntimeError, TypeError, NameError): go = False nptest.assert_equal(go, True)
def test_compare_yield_CH(): """ Tests :meth:`bet.postProcess.postTools.compare_yield` with column headings """ sample_quality = np.random.random((10, )) sort_ind = np.argsort(sample_quality) run_param = [] for i in range(10): run_param.append(np.random.random((4, ))) column_headings = ['swallow', 'coconut', 'ni', 'shrubbery'] try: postTools.compare_yield(sort_ind, sample_quality, run_param, column_headings) go = True except (RuntimeError, TypeError, NameError): go = False nptest.assert_equal(go, True)
# Run with varying transition sets bounds init_ratio = [0.1, 0.25, 0.5] min_ratio = [2e-3, 2e-5, 2e-8] max_ratio = [.5, .75, 1.0] tk_results = sampler.run_tk(init_ratio, min_ratio, max_ratio, rho_D, maximum, param_domain, kernel_rD, sample_save_file) # Run with varying increase/decrease ratios and tolerances for a rhoD_kernel increase = [1.0, 2.0, 4.0] decrease = [0.5, 0.5e2, 0.5e3] tolerance = [1e-4, 1e-6, 1e-8] incdec_results = sampler.run_inc_dec(increase, decrease, tolerance, rho_D, maximum, param_domain, transition_set, sample_save_file) # Compare the quality of several sets of samples result_list = [gen_results, tk_results, incdec_results] print("Compare yield of sample sets with various kernels") ptools.compare_yield(gen_results[3], gen_results[2], gen_results[4]) print("Compare yield of sample sets with various transition sets bounds") ptools.compare_yield(tk_results[3], tk_results[2], tk_results[4]) print("Compare yield of sample sets with variouos increase/decrease ratios") ptools.compare_yield(incdec_results[3], incdec_results[2], incdec_results[4]) # Read in points_ref and plot results p_ref = mdat['points_true'] p_ref = p_ref[:, 14]
# t_kernel, sample_save_file, reseed=3) # Run with varying transition sets bounds init_ratio = [0.1, 0.25, 0.5] min_ratio = [2e-3, 2e-5, 2e-8] max_ratio = [.5, .75, 1.0] tk_results = sampler.run_tk(init_ratio, min_ratio, max_ratio, rho_D, maximum, param_domain, kernel_rD, sample_save_file) # Run with varying increase/decrease ratios and tolerances for a rhoD_kernel increase = [1.0, 2.0, 4.0] decrease = [0.5, 0.5e2, 0.5e3] tolerance = [1e-4, 1e-6, 1e-8] incdec_results = sampler.run_inc_dec(increase, decrease, tolerance, rho_D, maximum, param_domain, transition_set, sample_save_file) # Compare the quality of several sets of samples result_list = [gen_results, tk_results, incdec_results] print("Compare yield of sample sets with various kernels") ptools.compare_yield(gen_results[3], gen_results[2], gen_results[4]) print("Compare yield of sample sets with various transition sets bounds") ptools.compare_yield(tk_results[3], tk_results[2], tk_results[4]) print("Compare yield of sample sets with variouos increase/decrease ratios") ptools.compare_yield(incdec_results[3], incdec_results[2], incdec_results[4]) # Read in points_ref and plot results p_ref = mdat['points_true'] p_ref = p_ref[:, 14]