def _evaluate (indata, prefix): util.set_and_train_kernel(indata) kmatrix=sg('get_kernel_matrix', 'TRAIN') km_train=max(abs(indata['kernel_matrix_train']-kmatrix).flat) kmatrix=sg('get_kernel_matrix', 'TEST') km_test=max(abs(indata['kernel_matrix_test']-kmatrix).flat) return util.check_accuracy( indata[prefix+'accuracy'], km_train=km_train, km_test=km_test)
classified=classified) ######################################################################## # public ######################################################################## def test(indata): try: util.set_features(indata, 'kernel_') except NotImplementedError, e: print e return True util.set_and_train_kernel(indata) try: _set_regression(indata) except RuntimeError, e: print "%s is disabled/unavailable!" % indata['name'] return True try: _train(indata) except StandardError, e: print e return False return _evaluate(indata)
return util.check_accuracy(indata['regression_accuracy'], alphas=alphas, bias=bias, support_vectors=sv, classified=classified) ######################################################################## # public ######################################################################## def test (indata): try: util.set_features(indata, 'kernel_') except NotImplementedError, e: print e return True util.set_and_train_kernel(indata) try: _set_regression(indata) except RuntimeError, e: print "%s is disabled/unavailable!" % indata['name'] return True try: _train(indata) except StandardError, e: print e return False return _evaluate(indata)