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()
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:
# you may not use this file except in compliance with the License.
# 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
#
# Test the Lock method
#
db = GTM()
globalName = '^Capital("US")'
setValue = 'Washington'
db.lock(globalName)
db.set(globalName, setValue)
db.kill(globalName)
# load an iris dataset
iris = load_iris()
# input_dataset = pd.DataFrame(iris.data, columns=iris.feature_names)
input_dataset = iris.data
color = iris.target
# autoscaling
input_dataset = (input_dataset -
input_dataset.mean(axis=0)) / input_dataset.std(axis=0,
ddof=1)
# construct SGTM model
model = GTM(shape_of_map,
shape_of_rbf_centers,
variance_of_rbfs,
lambda_in_em_algorithm,
number_of_iterations,
display_flag,
sparse_flag=True)
model.fit(input_dataset)
if model.success_flag:
# calculate of responsibilities
responsibilities = model.responsibility(input_dataset)
# plot the mean of responsibilities
means = responsibilities.dot(model.map_grids)
plt.figure(figsize=figure.figaspect(1))
plt.scatter(means[:, 0], means[:, 1], c=color)
plt.ylim(-1.1, 1.1)
plt.xlim(-1.1, 1.1)
# # 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 # # Test the Set, Get and Kill methods # db = GTM() globalName = "^Capital" setValue = "London" db.set(globalName, setValue) getValue = db.get(globalName) print globalName, " = ", getValue db.kill(globalName)
if __name__ == "__main__":
#Get the call in table file
if 'gtm_access_ci' not in os.environ:
print "Please set environment variable gtm_access_ci to the location of gtm_access.ci"
exit(0)
if not os.path.exists(os.environ['gtm_access_ci']):
print "Error: {0} does not exist".format(os.environ['gtm_access_ci'])
exit(0)
os.environ['GTMCI'] = os.environ['gtm_access_ci']
#Change working folder to database location
if 'gtm_data_dir' not in os.environ:
print "Please set environment variable gtm_data_dir to the data folder"
if not os.path.exists(os.environ['gtm_data_dir']):
print "Error: {0} does not exist".format(os.environ['gtm_data_dir'])
exit(0)
os.chdir(os.environ['gtm_data_dir'])
db = GTM()
print "Enter email"
username = raw_input()
print "Enter password"
password = raw_input()
db.execute('d loginLowLevel^user("{0}","{1}")'.format(username, password))
print "authenticated=", db.get('%sess("authenticated")')
print "uid=", db.get('%sess("uid")')
# # 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 # # Test the Set and Get methods # db = GTM() globalName = "^Capital" setValue = "London" db.set( globalName, setValue ) getValue = db.get( globalName ) print globalName, " = ", getValue
<<<<<<< HEAD
# autoscaling
autoscaled_X = (original_X - original_X.mean(axis=0)) / original_X.std(axis=0, ddof=1)
autoscaled_y = (original_y - original_y.mean()) / original_y.std(ddof=1)
autoscaled_target_y_value = (target_y_value - original_y.mean()) / original_y.std(ddof=1)
=======
variables = np.c_[x, y]
# 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]
>>>>>>> 2.0.X
# 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), :]
<<<<<<< HEAD
# inverse analysis
estimated_x_mean, estimated_x_mode, responsibilities_inverse, py = model.inverse_gtmr(autoscaled_target_y_value)
estimated_x_mean = estimated_x_mean * original_X.std(axis=0, ddof=1) + original_X.mean(axis=0)
estimated_x_mode = estimated_x_mode * original_X.std(axis=0, ddof=1) + original_X.mean(axis=0)
# print("estimated x-mean: {0}".format(estimated_x_mean))
print("estimated x-mode: {0}".format(estimated_x_mode))
raw_y = 0.3 * original_X[:, 0] - 0.1 * original_X[:, 1] + 0.2 * original_X[:, 2]
original_y = raw_y + noise_ratio_of_y * raw_y.std(ddof=1) * np.random.randn(len(raw_y))
# plot
plt.rcParams["font.size"] = 18
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
p = ax.scatter(original_X[:, 0], original_X[:, 1], original_X[:, 2], c=original_y)
fig.colorbar(p)
plt.show()
# autoscaling
autoscaled_X = (original_X - original_X.mean(axis=0)) / original_X.std(axis=0, ddof=1)
autoscaled_target_y_value = (target_y_value - original_y.mean(axis=0)) / original_y.std(axis=0, ddof=1)
# construct GTM 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_X)
if model.success_flag:
# calculate of responsibilities
responsibilities = model.responsibility(autoscaled_X)
# plot the mean of responsibilities
means = responsibilities.dot(model.map_grids)
plt.figure()
# plt.figure(figsize=figure.figaspect(1))
plt.scatter(means[:, 0], means[:, 1], c=original_y)
plt.colorbar()
plt.ylim(-1.1, 1.1)
plt.xlim(-1.1, 1.1)
plt.xlabel("z1 (mean)")
plt.ylabel("z2 (mean)")
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# 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
#
# Test the Set and Get methods
#
db = GTM()
db.set("^Capital","London")
capital = db.get("^Capital")
print "Capital = ", capital
#
# 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):
parameters_and_k3nerror = []
all_calculation_numbers = len(candidates_of_shape_of_map) * len(
candidates_of_shape_of_rbf_centers) * len(
candidates_of_variance_of_rbfs) * len(
candidates_of_lambda_in_em_algorithm)
calculation_number = 0
for shape_of_map_grid in candidates_of_shape_of_map:
for shape_of_rbf_centers_grid in candidates_of_shape_of_rbf_centers:
for variance_of_rbfs_grid in candidates_of_variance_of_rbfs:
for lambda_in_em_algorithm_grid in candidates_of_lambda_in_em_algorithm:
calculation_number += 1
print([calculation_number, all_calculation_numbers])
# construct GTM model
model = GTM(
[shape_of_map_grid, shape_of_map_grid],
[shape_of_rbf_centers_grid, shape_of_rbf_centers_grid],
variance_of_rbfs_grid, lambda_in_em_algorithm_grid,
number_of_iterations, display_flag)
model.fit(input_dataset)
if model.success_flag:
# calculate of responsibilities
responsibilities = model.responsibility(input_dataset)
# calculate the mean of responsibilities
means = responsibilities.dot(model.map_grids)
# calculate k3n-error
k3nerror_of_gtm = k3nerror(input_dataset, means,
k_in_k3nerror)
else:
k3nerror_of_gtm = 10**100
parameters_and_k3nerror.append([
shape_of_map_grid, shape_of_rbf_centers_grid,
# 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
#
# Test the Lock method
#
db = GTM()
globalName = "^ValueCounter"
setValue = "0"
getValue = "Initially empty"
db.lock( globalName )
db.set( globalName, setValue )
for i in xrange(0,9):
db.execute("set ^ValueCounter=^ValueCounter+1")
getValue = db.get( globalName )
print "counter = ", getValue
print "Final Counter Value = ", getValue
# 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
#
# Test the Lock method
#
db = GTM()
globalName = "^ValueCounter"
setValue = "0"
getValue = "Initially empty"
db.lock(globalName)
db.set(globalName, setValue)
for i in xrange(0, 9):
db.execute("set ^ValueCounter=^ValueCounter+1")
getValue = db.get(globalName)
print "counter = ", getValue
print "Final Counter Value = ", getValue
# 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 # # Test the Execute method # db = GTM() # # Exercise the string API # textOfCode = 'write $ZVERSION,!' db.execute( textOfCode ) # # Exercise the same pattern with direct strings # db.execute( 'write $ZVERSION,!')
# 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
#
# Test the Execute method
#
db = GTM()
#
# Exercise the string API
#
textOfCode = 'write $ZVERSION,!'
db.execute(textOfCode)
#
# Exercise the same pattern with direct strings
#
db.execute('write $ZVERSION,!')
# Copyright OSEHRA
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# 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
#
# Test the Set and Get methods
#
db = GTM()
db.set("^Capital", "London")
capital = db.get("^Capital")
print "Capital = ", capital
plt.show()
# divide a dataset into training data and test data
Xtrain = original_X[:500, :]
ytrain = original_y[:500]
Xtest = original_X[500:, :]
ytest = original_y[500:]
# autoscaling
# autoscaled_X = (original_X - original_X.mean(axis=0)) / original_X.std(axis=0,ddof=1)
autoscaled_Xtrain = (Xtrain - Xtrain.mean(axis=0)) / Xtrain.std(axis=0, ddof=1)
# autoscaled_Xtest = (Xtest - X.mean(axis=0)) / X.std(axis=0,ddof=1)
# autoscaled_ytrain = (ytrain - ytrain.mean()) / ytrain.std(ddof=1)
# construct GTM 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_Xtrain)
if model.success_flag:
# calculate of responsibilities
responsibilities = model.responsibility(autoscaled_Xtrain)
# plot the mean of responsibilities
means = responsibilities.dot(model.map_grids)
plt.figure()
# plt.figure(figsize=figure.figaspect(1))
plt.scatter(means[:, 0], means[:, 1], c=ytrain)
plt.colorbar()
plt.ylim(-1.1, 1.1)
plt.xlim(-1.1, 1.1)
plt.xlabel("z1 (mean)")
plt.ylabel("z2 (mean)")
# 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.
#
#=========================================================================*/
import sys
# load gtm module
from gtm import GTM
#
# Test the Set, Get and Order methods
#
db = GTM()
#
# Exercise the string API
#
globalName = '^Capital("US")'
setValue = 'Washington'
db.set( globalName, setValue )
globalName = '^Capital("UK")'
setValue = 'London'
db.set( globalName, setValue )
# 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 # # Simply test constructor, destructor and connection to GT.M # db = GTM() version = db.version() print "Version = ", version about = db.about() print "About = ", about
# 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):
#
# 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
#
# Test the Lock method
#
db = GTM()
globalName = '^Capital("US")'
setValue = 'Washington'
db.lock( globalName )
db.set( globalName, setValue )
db.kill( globalName )
