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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# 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
#
# 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.
# ==============================================================================
"""Boston housing price regression dataset.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.keras.utils.data_utils import get_file
from tensorflow.python.util.tf_export import tf_export
@tf_export('keras.datasets.boston_housing.load_data')
def load_data(path='boston_housing.npz', test_split=0.2, seed=113):
"""Loads the Boston Housing dataset.
Arguments:
path: path where to cache the dataset locally
(relative to ~/.keras/datasets).
test_split: fraction of the data to reserve as test set.
seed: Random seed for shuffling the data
before computing the test split.
Returns:
Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
"""
assert 0 <= test_split < 1
origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
path = get_file(
path,
origin=origin_folder + 'boston_housing.npz',
file_hash=
'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')
f = np.load(path)
x = f['x']
y = f['y']
f.close()
np.random.seed(seed)
indices = np.arange(len(x))
np.random.shuffle(indices)
x = x[indices]
y = y[indices]
x_train = np.array(x[:int(len(x) * (1 - test_split))])
y_train = np.array(y[:int(len(x) * (1 - test_split))])
x_test = np.array(x[int(len(x) * (1 - test_split)):])
y_test = np.array(y[int(len(x) * (1 - test_split)):])
return (x_train, y_train), (x_test, y_test)
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