# ==============================================================================

# Copyright 2014 Intel Corporation

#

# 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

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# ==============================================================================

# daal4py PCA example for distributed memory systems; SPMD mode

# run like this:

# mpirun -n 4 python ./pca_spmd.py

from pathlib import Path

from numpy import allclose, loadtxt

import daal4py as d4p

if __name__ == "__main__":

# Initialize SPMD mode

d4p.daalinit()

# Each process gets its own data

data_path = Path(__file__).parent / "data" / "distributed"

infile = data_path / f"pca_normalized_{d4p.my_procid() + 1}.csv"

# configure a PCA object to use svd instead of default correlation

algo = d4p.pca(method="svdDense", distributed=True)

# let's provide a file directly, not a table/array

result1 = algo.compute(str(infile))

# We can also load the data ourselfs and provide the numpy array

data = loadtxt(infile, delimiter=",")

result2 = algo.compute(data)

# PCA result objects provide eigenvalues, eigenvectors, means and variances

assert allclose(result1.eigenvalues, result2.eigenvalues)

assert allclose(result1.eigenvectors, result2.eigenvectors)

assert (

result1.means is None

and result2.means is None

or allclose(result1.means, result2.means)

)

assert (

result1.variances is None

and result2.variances is None

or allclose(result1.variances, result2.variances)

)

print("All looks good!")

d4p.daalfini()