8184b9fdf51e3f75835fe1f2d56c294d16686241,examples/federated_learning_with_encryption.py,,,#,162
Before Change
// Each client trains a linear regressor on its own data
for (i, c) in enumerate(clients):
c = c.fit(X[i], y[i], n_iter=50, eta=0.05)
print(c.weights)
// Predict
for (i, c) in enumerate(clients):
After Change
clients[2].gradient_step(aggr)
for (i, c) in enumerate(clients):
y_pred = c.predict(c.X)
print(mean_square_error(y_pred, c.y))
for (i, c) in enumerate(clients):
y_pred = c.predict(X_test)
print(mean_square_error(y_pred, y_test))
In pattern: SUPERPATTERN
Frequency: 3
Non-data size: 4
Instances Project Name: data61/python-paillier
Commit Name: 8184b9fdf51e3f75835fe1f2d56c294d16686241
Time: 2017-06-20
Author: giorgio.patrini@anu.edu.au
File Name: examples/federated_learning_with_encryption.py
Class Name:
Method Name:
Project Name: titu1994/DenseNet
Commit Name: 516c2a4c7e8f92e1ea299e966215c2ffe4c5b980
Time: 2016-12-07
Author: titu1994@gmail.com
File Name: cifar10.py
Class Name:
Method Name:
Project Name: data61/python-paillier
Commit Name: eb4ffb6cdaf6f04f978fc57e32c95c8b4a33bcb6
Time: 2017-06-20
Author: giorgio.patrini@anu.edu.au
File Name: examples/federated_learning_with_encryption.py
Class Name:
Method Name: