#
# Copyright 2019 GridGain Systems, Inc. and Contributors.
#
# Licensed under the GridGain Community Edition License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.gridgain.com/products/software/community-edition/gridgain-community-edition-license
#
# 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.
#

import numpy as np
from sklearn.datasets import make_regression

with Ignite("example-ignite.xml") as ignite:
    cache = ignite.create_cache("my-cache")
    for i, row in enumerate(np.column_stack(make_regression())):
        cache.put(i, row)

with Ignite("example-ignite.xml") as ignite:
    cache = ignite.create_cache("my-cache", parts=10)
    for i, row in enumerate(np.column_stack(make_regression())):
        cache.put(i, row)

with Ignite("example-ignite.xml") as ignite:
    cache = ignite.create_cache("my-cache", parts=10)
    for i, row in enumerate(np.column_stack(make_regression())):
        cache.put(i, row)