def condition(X, y, kernelFn, hyper_opt_config_copys):
assert len(y.shape) == 1 # y must be shapeless (n, )
h_kernel, noise_std = hyper_opt_config_copys
kernel = lambda x1, x2: kernelFn(x1, x2, h_kernel)
noise_vector = predict.noise_vector(X, noise_std)
L = linalg.cholesky(X, kernel, noise_vector)
alpha = predict.alpha(y, L)
return types.RegressionParams(X, L, alpha, kernel, y, noise_std)
def condition(X, y, kernelFn, hyper_opt_config_copys):
assert len(y.shape) == 1 #y must be shapeless (n, )
h_kernel, noise_std = hyper_opt_config_copys
kernel = lambda x1, x2: kernelFn(x1, x2, h_kernel)
noise_vector = predict.noise_vector(X, noise_std)
L = linalg.cholesky(X, kernel, noise_vector)
alpha = predict.alpha(y, L)
return types.RegressionParams(X, L, alpha, kernel, y, noise_std)
def add_data(newX, newY, regressor, query=None, insertionID=None):
assert (isinstance(regressor, types.RegressionParams))
assert (not query or isinstance(query, types.QueryParams))
assert (len(newX.shape) == 2)
assert (len(newY.shape) == 1)
if not (insertionID): #No insterionID provide. Append data to the end.
# Compute the new rows and columns of the covariance matrix
Kxn = regressor.kernel(regressor.X, newX)
Knn = regressor.kernel(newX, newX)
nn_noise_std = predict.noise_vector(newX, regressor.noise_std)
# Update the regression opt_config_copys
regressor.X = np.vstack((regressor.X, newX))
regressor.y = np.hstack((regressor.y, newY))
regressor.L = chol_up(regressor.L, Kxn, Knn, nn_noise_std)
# sadly, this is still expensive. However osborne's thesis appendix B can
# be used to speed up this step too. Maybe by a factor of 2.
regressor.alpha = predict.alpha(regressor.y, regressor.L)
# Optionally update the query
if query is not None:
Kxsn = regressor.kernel(newX, query.Xs)
query.K_xxs = np.vstack((query.K_xxs, Kxsn))
else:
# Compute the new rows and columns of the covariance matrix
Kx1n = regressor.kernel(regressor.X[:insertionID, :], newX)
Knx2 = regressor.kernel(newX, regressor.X[insertionID:, :])
Knn = regressor.kernel(newX, newX)
nn_noise_std = predict.noise_vector(newX, regressor.noise_std)
regressor.X = np.vstack(
(regressor.X[:insertionID, :], newX, regressor.X[insertionID:, :]))
regressor.y = np.hstack(
(regressor.y[:insertionID], newY, regressor.y[insertionID:]))
regressor.L = chol_up_insert(regressor.L, Kx1n, Knx2, Knn,
nn_noise_std, insertionID)
# sadly, this is still expensive. However osborne's thesis appendix B can
# be used to speed up this step too. Maybe by a factor of 2.
regressor.alpha = predict.alpha(regressor.y, regressor.L)
if query is not None:
Kxsn = regressor.kernel(newX, query.Xs)
query.K_xxs = np.vstack((query.K_xxs[:insertionID, :], Kxsn,
query.K_xxs[insertionID:, :]))
def criterion(sigma, noise):
k = lambda x1, x2: cov_fn(x1, x2, sigma)
X_noise = predict.noise_vector(X, noise)
L = linalg.cholesky(X, k, X_noise)
a = predict.alpha(Y, L)
if optCrition == "logMarg":
val = negative_log_marginal_likelihood(Y, L, a)
elif optCrition == "crossVal":
val = negative_log_prob_cross_val(Y, L, a)
if verbose is True:
print("[" + str(val) + "] ", sigma, noise)
return val
def criterion(sigma, noise):
k = lambda x1, x2: cov_fn(x1, x2, sigma)
X_noise = predict.noise_vector(X, noise)
L = linalg.cholesky(X, k, X_noise)
a = predict.alpha(Y, L)
if optCrition == 'logMarg':
val = negative_log_marginal_likelihood(Y, L, a)
elif optCrition == 'crossVal':
val = negative_log_prob_cross_val(Y, L, a)
if verbose is True:
print('[' + str(val) + '] ', sigma, noise)
return val
def add_data(newX, newY, regressor, query=None, insertionID=None):
assert isinstance(regressor, types.RegressionParams)
assert not query or isinstance(query, types.QueryParams)
assert len(newX.shape) == 2
assert len(newY.shape) == 1
if not (insertionID): # No insterionID provide. Append data to the end.
# Compute the new rows and columns of the covariance matrix
Kxn = regressor.kernel(regressor.X, newX)
Knn = regressor.kernel(newX, newX)
nn_noise_std = predict.noise_vector(newX, regressor.noise_std)
# Update the regression opt_config_copys
regressor.X = np.vstack((regressor.X, newX))
regressor.y = np.hstack((regressor.y, newY))
regressor.L = chol_up(regressor.L, Kxn, Knn, nn_noise_std)
# sadly, this is still expensive. However osborne's thesis appendix B can
# be used to speed up this step too. Maybe by a factor of 2.
regressor.alpha = predict.alpha(regressor.y, regressor.L)
# Optionally update the query
if query is not None:
Kxsn = regressor.kernel(newX, query.Xs)
query.K_xxs = np.vstack((query.K_xxs, Kxsn))
else:
# Compute the new rows and columns of the covariance matrix
Kx1n = regressor.kernel(regressor.X[:insertionID, :], newX)
Knx2 = regressor.kernel(newX, regressor.X[insertionID:, :])
Knn = regressor.kernel(newX, newX)
nn_noise_std = predict.noise_vector(newX, regressor.noise_std)
regressor.X = np.vstack((regressor.X[:insertionID, :], newX, regressor.X[insertionID:, :]))
regressor.y = np.hstack((regressor.y[:insertionID], newY, regressor.y[insertionID:]))
regressor.L = chol_up_insert(regressor.L, Kx1n, Knx2, Knn, nn_noise_std, insertionID)
# sadly, this is still expensive. However osborne's thesis appendix B can
# be used to speed up this step too. Maybe by a factor of 2.
regressor.alpha = predict.alpha(regressor.y, regressor.L)
if query is not None:
Kxsn = regressor.kernel(newX, query.Xs)
query.K_xxs = np.vstack((query.K_xxs[:insertionID, :], Kxsn, query.K_xxs[insertionID:, :]))
def remove_data(regressor, remID, query=None):
assert isinstance(regressor, types.RegressionParams)
assert not query or isinstance(query, types.QueryParams)
regressor.X = np.delete(regressor.X, remID, axis=0)
regressor.y = np.delete(regressor.y, remID, axis=0)
# regressor.L = chol_down(regressor.L, remID)
noise_vector = predict.noise_vector(regressor.X, regressor.noise_std)
regressor.L = linalg.cholesky(regressor.X, regressor.kernel, noise_vector)
regressor.alpha = predict.alpha(regressor.y, regressor.L)
# Optionally update the query
if query is not None:
query.K_xxs = np.delete(query.K_xxs, remID, axis=0)
def remove_data(regressor, remID, query=None):
assert (isinstance(regressor, types.RegressionParams))
assert (not query or isinstance(query, types.QueryParams))
regressor.X = np.delete(regressor.X, remID, axis=0)
regressor.y = np.delete(regressor.y, remID, axis=0)
# regressor.L = chol_down(regressor.L, remID)
noise_vector = predict.noise_vector(regressor.X, regressor.noise_std)
regressor.L = linalg.cholesky(regressor.X, regressor.kernel, noise_vector)
regressor.alpha = predict.alpha(regressor.y, regressor.L)
# Optionally update the query
if query is not None:
query.K_xxs = np.delete(query.K_xxs, remID, axis=0)
def criterion(sigma, noise):
k = lambda x1, x2: cov_fn(x1, x2, sigma)
val = 0
for f in range(folds.n_folds):
Xf = folds.X[f]
Yf = folds.flat_y[f]
Xf_noise = predict.noise_vector(Xf, noise)
Lf = linalg.cholesky(Xf, k, Xf_noise)
af = predict.alpha(Yf, Lf)
if optCrition == "logMarg":
val += negative_log_marginal_likelihood(Yf, Lf, af)
elif optCrition == "crossVal":
val += negative_log_prob_cross_val(Yf, Lf, af)
if verbose is True:
print("[" + str(val) + "] ", sigma, noise)
return val
def criterion(sigma, noise):
k = lambda x1, x2: cov_fn(x1, x2, sigma)
val = 0
for f in range(folds.n_folds):
Xf = folds.X[f]
Yf = folds.flat_y[f]
Xf_noise = predict.noise_vector(Xf, noise)
Lf = linalg.cholesky(Xf, k, Xf_noise)
af = predict.alpha(Yf, Lf)
if optCrition == 'logMarg':
val += negative_log_marginal_likelihood(Yf, Lf, af)
elif optCrition == 'crossVal':
val += negative_log_prob_cross_val(Yf, Lf, af)
if verbose is True:
print('[' + str(val) + '] ', sigma, noise)
return val
