git clone https://github.com/IntelPython/daal4py.git
cd daal4py
conda create -n DAAL4PY -c intel -c intel/label/test -c conda-forge python=3.6 mpich cnc tbb-devel daal daal-include cython jinja2 numpy
source activate DAAL4PY
export CNCROOT=$CONDA_PREFIX
export TBBROOT=$CONDA_PREFIX
export DAALROOT=$CONDA_PREFIX
python setup.py build_ext
python setup.py install
# 运行后面的demosource deactivate DAAL4PY # 退出

#*******************************************************************************
# Copyright 2014-2018 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 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.
#*******************************************************************************# daal4py Decision Forest Classification example for shared memory systemsimport daal4py as d4p
import numpy as np# let's try to use pandas' fast csv reader
try:import pandasread_csv = lambda f, c: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=np.float32).values
except:# fall back to numpy loadtxtread_csv = lambda f, c: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2, dtype=np.float32)def main():# input data fileinfile = "./data/batch/df_classification_train.csv"testfile = "./data/batch/df_classification_test.csv"# Configure a training object (5 classes)train_algo = d4p.decision_forest_classification_training(5, nTrees=10, minObservationsInLeafNode=8, featuresPerNode=3, engine = d4p.engines_mt19937(seed=777),varImportance='MDI', bootstrap=True, resultsToCompute='computeOutOfBagError')# Read data. Let's use 3 features per observationdata   = read_csv(infile, range(3))labels = read_csv(infile, range(3,4))train_result = train_algo.compute(data, labels)# Traiing result provides (depending on parameters) model, outOfBagError, outOfBagErrorPerObservation and/or variableImportance# Now let's do some predictionpredict_algo = d4p.decision_forest_classification_prediction(5)# read test data (with same #features)pdata = read_csv(testfile, range(3))plabels = read_csv(testfile, range(3,4))# now predict using the model from the training abovepredict_result = predict_algo.compute(pdata, train_result.model)# Prediction result provides predictionassert(predict_result.prediction.shape == (pdata.shape[0], 1))return (train_result, predict_result, plabels)if __name__ == "__main__":(train_result, predict_result, plabels) = main()print("\nVariable importance results:\n", train_result.variableImportance)print("\nOOB error:\n", train_result.outOfBagError)print("\nDecision forest prediction results (first 10 rows):\n", predict_result.prediction[0:10])print("\nGround truth (first 10 rows):\n", plabels[0:10])print('All looks good!')

0.00125126,0.563585,8,2,
0.193304,0.808741,12,1,
0.585009,0.479873,6,1,
0.350291,0.895962,13,4,
0.82284,0.746605,11,2,
0.174108,0.858943,12,0,
0.710501,0.513535,10,2,
0.303995,0.0149846,1,2,
0.0914029,0.364452,4,0,
0.147313,0.165899,0,4,
0.988525,0.445692,7,2,
0.119083,0.00466933,0,2,
0.0089114,0.37788,4,2,
0.531663,0.571184,10,3,
0.601764,0.607166,10,4,
0.166234,0.663045,8,4,
0.450789,0.352123,5,3,
0.0570391,0.607685,8,4,
0.783319,0.802606,15,3,
0.519883,0.30195,6,2,
0.875973,0.726676,11,1,
0.955901,0.925718,15,3,
0.539354,0.142338,2,3,
0.462081,0.235328,1,2,
0.862239,0.209601,3,1,
0.779656,0.843654,15,3,
0.996796,0.999695,15,2,
0.611499,0.392438,6,0,
0.266213,0.297281,5,2,
0.840144,0.0237434,3,1,
0.375866,0.0926237,1,0,
0.677206,0.0562151,2,3,
0.00878933,0.91879,12,2,
0.275887,0.272897,5,2,
0.587909,0.691183,10,4,
0.837611,0.726493,11,1,
0.484939,0.205359,1,2,
0.743736,0.468459,6,2,
0.457961,0.949156,13,3,
0.744438,0.10828,2,2,
0.599048,0.385235,6,0,
0.735008,0.608966,10,2,
0.572405,0.361339,6,0,
0.151555,0.225105,0,3,
0.425153,0.802881,13,3,

#*******************************************************************************# Copyright 2014-2018 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 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.#*******************************************************************************# daal4py low order moments example for shared memory systemsimport daal4py as d4pimport numpy as np# let's try to use pandas' fast csv readertry:import pandasread_csv = lambda f, c: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=np.float64).valuesexcept:# fall back to numpy loadtxtread_csv = lambda f, c: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)def main():# read data from filefile = "./data/batch/covcormoments_dense.csv"data = read_csv(file, range(10))# computealg = d4p.low_order_moments()res = alg.compute(data)# result provides minimum, maximum, sum, sumSquares, sumSquaresCentered,# mean, secondOrderRawMoment, variance, standardDeviation, variationassert res.minimum.shape == (1, data.shape[1])assert res.maximum.shape == (1, data.shape[1])assert res.sum.shape == (1, data.shape[1])assert res.sumSquares.shape == (1, data.shape[1])assert res.sumSquaresCentered.shape == (1, data.shape[1])assert res.mean.shape == (1, data.shape[1])assert res.secondOrderRawMoment.shape == (1, data.shape[1])assert res.variance.shape == (1, data.shape[1])assert res.standardDeviation.shape == (1, data.shape[1])assert res.variation.shape == (1, data.shape[1])return resif __name__ == "__main__":res = main()# print resultsprint("\nMinimum:\n", res.minimum)print("\nMaximum:\n", res.maximum)print("\nSum:\n", res.sum)print("\nSum of squares:\n", res.sumSquares)print("\nSum of squared difference from the means:\n", res.sumSquaresCentered)print("\nMean:\n", res.mean)print("\nSecond order raw moment:\n", res.secondOrderRawMoment)print("\nVariance:\n", res.variance)print("\nStandard deviation:\n", res.standardDeviation)print("\nVariation:\n", res.variation)print('All looks good!')