axis=1) # NaN を含む変数を削除
# 標準偏差が 0 の説明変数を削除
std_0_variable_flags = original_x.std() == 0
x = original_x.drop(original_x.columns[std_0_variable_flags], axis=1)
variables = pd.concat([y, x], axis=1)
numbers_of_x = np.arange(numbers_of_y[-1] + 1, variables.shape[1])
# standardize x and y
autoscaled_variables = (variables - variables.mean(axis=0)) / variables.std(
axis=0, ddof=1)
autoscaled_target_y_value = (target_y_value - variables.mean(
axis=0)[numbers_of_y]) / variables.std(axis=0, ddof=1)[numbers_of_y]
# construct GTMR model
model = GTM(shape_of_map, shape_of_rbf_centers, variance_of_rbfs,
lambda_in_em_algorithm, number_of_iterations, display_flag)
model.fit(autoscaled_variables)
if model.success_flag:
# calculate of responsibilities
responsibilities = model.responsibility(autoscaled_variables)
means = responsibilities.dot(model.map_grids)
modes = model.map_grids[responsibilities.argmax(axis=1), :]
mean_of_estimated_mean_of_y, mode_of_estimated_mean_of_y, responsibilities_y, py = \
model.gtmr_predict(autoscaled_variables.iloc[:, numbers_of_x], numbers_of_x, numbers_of_y)
plt.rcParams['font.size'] = 18
for index, y_number in enumerate(numbers_of_y):
predicted_y_test = mode_of_estimated_mean_of_y[:, index] * variables.iloc[:, y_number].std(
) + variables.iloc[:, y_number].mean()
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#=========================================================================*/
# load gtm module
from gtm import GTM
db = GTM()
print db.about()
print db.version()
getValue = "Initially empty"
for k in xrange(1, 1000):
db.set("^FibonacciA", "1")
db.set("^FibonacciB", "1")
termnumber = 100
for i in xrange(1, termnumber):
plt.xlabel('y1')
plt.ylabel('y2')
plt.show()
variables = np.c_[x, y1, y2]
variables_train, variables_test = train_test_split(
variables, test_size=number_of_test_samples, random_state=100)
# standardize x and y
autoscaled_variables_train = (variables_train - variables_train.mean(axis=0)
) / variables_train.std(axis=0, ddof=1)
autoscaled_variables_test = (variables_test - variables_train.mean(axis=0)
) / variables_train.std(axis=0, ddof=1)
# optimize hyperparameter in GTMR with CV
model = GTM()
model.gtmr_cv_opt(autoscaled_variables_train, numbers_of_y,
candidates_of_shape_of_map,
candidates_of_shape_of_rbf_centers,
candidates_of_variance_of_rbfs,
candidates_of_lambda_in_em_algorithm, fold_number,
number_of_iterations)
model.display_flag = display_flag
print('optimized shape of map :', model.shape_of_map)
print('optimized shape of RBF centers :', model.shape_of_rbf_centers)
print('optimized variance of RBFs :', model.variance_of_rbfs)
print('optimized lambda in EM algorithm :', model.lambda_in_em_algorithm)
# construct GTMR model
model.fit(autoscaled_variables_train)
if model.success_flag:
