Metrics and Statistics

topaz.metrics.average_precision(target, pred, N=None)

topaz.metrics.precision_recall_curve(target, pred, N=None)

topaz.stats.gmm_fit(x, pi=0.5, split=None, alpha=0.5, beta=0.5, scale=1, tol=0.001, num_iters=100, share_var=True, verbose=False)

topaz.stats.gmm_fit_numpy(x, pi=0.5, alpha=0.5, beta=0.5, tol=0.001, num_iters=50, verbose=False)

topaz.stats.norm_fit(x, alpha=900, beta=1, scale=1, num_iters=100, use_cuda=False, verbose=False)

topaz.stats.normalize(x, alpha=900, beta=1, num_iters=100, sample=1, method='gmm', use_cuda=False, verbose=False)