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# Copyright 2014-2019 Intel Corporation
# All Rights Reserved.
# This software is licensed under the Apache License, Version 2.0 (the
# "License"), the following terms apply:
# You may not use this file except in compliance with the License. You may
# obtain a copy of the License at
# 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.
# daal4py LBFGS (limited memory Broyden-Fletcher-Goldfarb-Shanno) example for shared memory systems
# using Mean Squared Error objective function
import daal4py as d4p
import numpy as np
# let's try to use pandas' fast csv reader
import pandas
read_csv = lambda f, c, t=np.float64: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
# fall back to numpy loadtxt
read_csv = lambda f, c, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
def main(readcsv=read_csv, method='defaultDense'):
infile = "./data/batch/lbfgs.csv"
# Read the data, let's have 10 independent variables
data = readcsv(infile, range(10))
dep_data = readcsv(infile, range(10,11))
nVectors = data.shape[0]
# configure a MSE object
mse_algo = d4p.optimization_solver_mse(nVectors)
mse_algo.setup(data, dep_data)
# configure an LBFGS object
sls = np.array([[1.0e-4]], dtype=np.double)
niters = 1000
lbfgs_algo = d4p.optimization_solver_lbfgs(mse_algo,
# finally do the computation
inp = np.array([[100]]*11, dtype=np.double)
res = lbfgs_algo.compute(inp)
# The LBFGS result provides minimum and nIterations
assert res.minimum.shape == inp.shape and res.nIterations[0][0] <= niters
return res
if __name__ == "__main__":
res = main()
print("\nExpected coefficients:\n", np.array([[11], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], dtype=np.double))
print("\nResulting coefficients:\n", res.minimum)
print("\nNumber of iterations performed:\n", res.nIterations[0][0])
print('All looks good!')
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