def test_derivfuncs_dims1(): import ann x = np.zeros((2,2,2)) y = np.zeros((2,2,2)) MSE = ann.ERROR_MSE failed = False try: ann.get_deriv(MSE, x, y) except ValueError: failed = True assert failed, "Should not work with more than 2 dimensions"
def test_derivfuncs_dims1(): import ann x = np.zeros((2, 2, 2)) y = np.zeros((2, 2, 2)) MSE = ann.ERROR_MSE failed = False try: ann.get_deriv(MSE, x, y) except ValueError: failed = True assert failed, "Should not work with more than 2 dimensions"
def test_derivfuncs_dims2(): import ann # First, should fail if arrays differ in dimension x = np.zeros((2,2)) y = np.zeros((3,3)) MSE = ann.ERROR_MSE failed = False try: ann.get_deriv(MSE, x, y) except ValueError: failed = True assert failed, "Dimensions should not match!"
def test_derivfuncs_dims2(): import ann # First, should fail if arrays differ in dimension x = np.zeros((2, 2)) y = np.zeros((3, 3)) MSE = ann.ERROR_MSE failed = False try: ann.get_deriv(MSE, x, y) except ValueError: failed = True assert failed, "Dimensions should not match!"
def test_derivfuncs_data1dlistsminimal(): import ann rows, cols = 1, 1 x = [0.0 for i in range(rows)] y = [2.0 for i in range(rows)] MSE = ann.ERROR_MSE error = ann.get_deriv(MSE, x, y) assert len(error.shape) == 1, "Should be one-dimensional result" assert error.shape[0] == rows, "Count should match row number" for e in error.ravel(): assert 0.000001 > e - (2 - 0), "Error is incorrect"
def test_derivfuncs_data1d(): import ann rows, cols = 5, 1 x = np.zeros(rows) y = np.ones(rows) * 2 MSE = ann.ERROR_MSE error = ann.get_deriv(MSE, x, y) assert len(error.shape) == 1, "Should be one-dimensional result" assert error.shape[0] == rows, "Count should match row number" for e in error.ravel(): assert 0.000001 > e - (2 - 0), "Error is incorrect"
def test_surv_likelihood(): import ann dim = (20,2) targets = np.ones(dim) censlvl=0.0 for i in range(len(targets)): if np.random.uniform() < censlvl: targets[i, 1] = 0 outputs = np.random.normal(1, 10, size=dim) #timesorting = outputs[:, 0].argsort() cens = targets[:, 1] < 1 uncens = targets[:, 1] == 1 errors = ann.get_error(ann.ERROR_SURV_LIKELIHOOD, targets, outputs) derivs = ann.get_deriv(ann.ERROR_SURV_LIKELIHOOD, targets, outputs)
def test_surv_likelihood(): import ann dim = (20, 2) targets = np.ones(dim) censlvl = 0.0 for i in range(len(targets)): if np.random.uniform() < censlvl: targets[i, 1] = 0 outputs = np.random.normal(1, 10, size=dim) #timesorting = outputs[:, 0].argsort() cens = targets[:, 1] < 1 uncens = targets[:, 1] == 1 errors = ann.get_error(ann.ERROR_SURV_LIKELIHOOD, targets, outputs) derivs = ann.get_deriv(ann.ERROR_SURV_LIKELIHOOD, targets, outputs)