CMSIS-DSP: Added Bayes benchmarks

Testing/CMakeLists.txt

@@ -150,6 +150,7 @@ set (NNSRC
    Source/Benchmarks/ComplexMathsBenchmarksF32.cpp
    Source/Benchmarks/ComplexMathsBenchmarksQ31.cpp
    Source/Benchmarks/ComplexMathsBenchmarksQ15.cpp
+   Source/Benchmarks/BayesF32.cpp
    Source/Benchmarks/FIRF32.cpp
    Source/Benchmarks/FIRQ31.cpp
    Source/Benchmarks/FIRQ15.cpp

Testing/Include/Benchmarks/BayesF32.h

@@ -0,0 +1,37 @@
+#include "Test.h"
+#include "Pattern.h"
+
+#include "dsp/bayes_functions.h"
+
+class BayesF32:public Client::Suite
+    {
+        public:
+            BayesF32(Testing::testID_t id);
+            virtual void setUp(Testing::testID_t,std::vector<Testing::param_t>& paramsArgs,Client::PatternMgr *mgr);
+            virtual void tearDown(Testing::testID_t,Client::PatternMgr *mgr);
+        private:
+            #include "BayesF32_decl.h"
+            
+            Client::Pattern<float32_t> input;
+            Client::Pattern<float32_t> params;
+            Client::Pattern<int16_t> dims;
+
+            Client::LocalPattern<float32_t> outputProbas;
+            Client::LocalPattern<int16_t> outputPredicts;
+
+            // Reference patterns are not loaded when we are in dump mode
+            Client::RefPattern<int16_t> predicts;
+
+            int classNb,vecDim;
+            int nb=0;
+            const float32_t *theta;
+            const float32_t *sigma;
+            const float32_t *classPrior;
+            float32_t epsilon;
+
+            arm_gaussian_naive_bayes_instance_f32 bayes;
+
+            const float32_t *inp;
+            float32_t *bufp;
+
+    };

Testing/Parameters/DSP/Bayes/BayesF16/Params1.txt

@@ -0,0 +1,19 @@
+18
+12
+3
+12
+5
+12
+4
+14
+3
+14
+5
+14
+4
+20
+3
+20
+5
+20
+4

Testing/Parameters/DSP/Bayes/BayesF16/Params2.txt

@@ -0,0 +1,19 @@
+18
+12
+3
+12
+5
+12
+4
+14
+3
+14
+5
+14
+4
+20
+3
+20
+5
+20
+4

Testing/Parameters/DSP/Bayes/BayesF32/Params1.txt

@@ -0,0 +1,19 @@
+18
+12
+3
+12
+5
+12
+4
+14
+3
+14
+5
+14
+4
+20
+3
+20
+5
+20
+4

Testing/Parameters/DSP/Bayes/BayesF32/Params2.txt

@@ -0,0 +1,19 @@
+18
+12
+3
+12
+5
+12
+4
+14
+3
+14
+5
+14
+4
+20
+3
+20
+5
+20
+4

Testing/PatternGeneration/Bayes.py

@@ -142,6 +142,52 @@ def generateNewTest(config,nb):
 def writeTests(config):
     generateNewTest(config,1)
 
+def writeBenchmark(config):
+    someLists=[VECDIM,BAYESCLASSES]
+    
+    r=np.array([element for element in itertools.product(*someLists)])
+    nbtests=len(VECDIM)*len(BAYESCLASSES)*2
+    config.writeParam(2, r.reshape(nbtests))
+
+    params=[]
+    inputs=[] 
+    referencepredict=[]
+    dims=[] 
+    nbin=0
+    nbparam=0;
+
+    for vecDim, classNb in r:
+        gb = trainGaussian(classNb,vecDim)
+        p = []
+        p += list(np.reshape(gb.theta_,np.size(gb.theta_)))
+        p += list(np.reshape(gb.sigma_,np.size(gb.sigma_)))
+        p += list(np.reshape(gb.class_prior_,np.size(gb.class_prior_)))
+        p.append(gb.epsilon_)
+
+        params += p
+        dims += [nbin,nbparam]
+        nbparam = nbparam + len(p)
+
+        v,c=newRandomVector(classNb,vecDim,PREDICTRATIO)
+        inputs += v
+
+        nbin = nbin + len(v)
+
+        y_pred = gb.predict([v])
+        referencepredict.append(y_pred[0])
+       
+
+    inputs = np.array(inputs)
+    params = np.array(params)
+    referencepredict = np.array(referencepredict)
+    dims = np.array(dims)
+
+    config.writeInput(2, inputs,"Inputs")
+    config.writeReferenceS16(2, referencepredict,"Predicts")
+    config.writeReference(2, params,"Params")
+    config.writeInputS16(2, dims,"DimsBench")
+
+
 def generatePatterns():
     PATTERNDIR = os.path.join("Patterns","DSP","Bayes","Bayes")
     PARAMDIR = os.path.join("Parameters","DSP","Bayes","Bayes")
@@ -152,5 +198,8 @@ def generatePatterns():
     writeTests(configf32)
     writeTests(configf16)
 
+    writeBenchmark(configf32)
+    writeBenchmark(configf16)
+
 if __name__ == '__main__':
   generatePatterns()

Testing/Patterns/DSP/Bayes/BayesF16/DimsBench2_s16.txt

@@ -0,0 +1,38 @@
+H
+18
+// 0
+0x0000
+// 0
+0x0000
+// 12
+0x000C
+// 76
+0x004C
+// 24
+0x0018
+// 202
+0x00CA
+// 36
+0x0024
+// 303
+0x012F
+// 50
+0x0032
+// 391
+0x0187
+// 64
+0x0040
+// 537
+0x0219
+// 78
+0x004E
+// 654
+0x028E
+// 98
+0x0062
+// 778
+0x030A
+// 118
+0x0076
+// 984
+0x03D8

Testing/Patterns/DSP/Bayes/BayesF16/Inputs2_f16.txt

@@ -0,0 +1,278 @@
+H
+138
+// -0.040186
+0xa925
+// 1.073982
+0x3c4c
+// 0.028277
+0x273d
+// 0.011638
+0x21f5
+// 0.028431
+0x2747
+// 0.025892
+0x26a1
+// 0.019511
+0x24ff
+// 0.010574
+0x216a
+// -0.000066
+0x8455
+// 0.046097
+0x29e6
+// 0.016035
+0x241b
+// -0.053561
+0xaadb
+// 0.013593
+0x22f6
+// 0.021897
+0x259b
+// 1.003003
+0x3c03
+// 0.038005
+0x28dd
+// -0.023690
+0xa611
+// -0.011551
+0xa1ea
+// 0.025625
+0x268f
+// 0.012535
+0x226b
+// 0.024917
+0x2661
+// -0.011525
+0xa1e7
+// -0.073949
+0xacbc
+// 0.022863
+0x25da
+// -0.048628
+0xaa39
+// 0.969280
+0x3bc1
+// -0.051026
+0xaa88
+// 0.049948
+0x2a65
+// -0.001735
+0x971b
+// 0.028726
+0x275b
+// -0.024842
+0xa65c
+// -0.013571
+0xa2f3
+// -0.007444
+0x9f9f
+// 0.012261
+0x2247
+// -0.006992
+0x9f29
+// -0.018144
+0xa4a5
+// 0.063726
+0x2c14
+// 0.014435
+0x2364
+// 1.070055
+0x3c48
+// 0.011378
+0x21d3
+// 0.020624
+0x2548
+// 0.012175
+0x223c
+// -0.060880
+0xabcb
+// -0.003673
+0x9b86
+// 0.066080
+0x2c3b
+// -0.030483
+0xa7ce
+// -0.026682
+0xa6d5
+// -0.041411
+0xa94d
+// 0.030163
+0x27b9
+// -0.024941
+0xa663
+// -0.006896
+0x9f10
+// -0.024445
+0xa642
+// 0.018958
+0x24da
+// -0.014716
+0xa389
+// 1.017171
+0x3c12
+// -0.024220
+0xa633
+// -0.043287
+0xa98a
+// 0.029118
+0x2774
+// -0.008586
+0xa065
+// 0.029317
+0x2781
+// -0.000696
+0x91b4
+// 0.007286
+0x1f76
+// -0.044731
+0xa9ba
+// -0.024640
+0xa64f
+// -0.010476
+0xa15d
+// 0.005055
+0x1d2d
+// 0.038301
+0x28e7
+// 0.931977
+0x3b75
+// -0.028171
+0xa736
+// 0.037370
+0x28c9
+// -0.041713
+0xa957
+// -0.039430
+0xa90c
+// 0.034133
+0x285e
+// -0.059803
+0xaba8
+// -0.008422
+0xa050
+// 0.035178
+0x2881
+// 0.048290
+0x2a2e
+// -0.018230
+0xa4ab
+// 0.004761
+0x1ce0
+// -0.034561
+0xa86c
+// 0.977494
+0x3bd2
+// -0.056634
+0xab40
+// 0.020863
+0x2557
+// 0.039257
+0x2906
+// 0.034689
+0x2871
+// 0.007272
+0x1f72
+// 0.072741
+0x2ca8
+// 0.011958
+0x221f
+// -0.004184
+0x9c49
+// 0.080404
+0x2d25
+// -0.093746
+0xae00
+// 0.046717
+0x29fb
+// 0.034230
+0x2862
+// 0.037576
+0x28cf
+// -0.030905
+0xa7e9
+// -0.002138
+0x9861
+// -0.014129
+0xa33c
+// 0.014546
+0x2373
+// 0.006195
+0x1e58
+// 0.979450
+0x3bd6
+// 0.008912
+0x2090
+// -0.036559
+0xa8ae
+// -0.027870
+0xa722
+// -0.017747
+0xa48b
+// 0.050483
+0x2a76
+// 0.073055
+0x2cad
+// 0.046158
+0x29e9
+// -0.009757
+0xa0ff
+// 0.085861
+0x2d7f
+// -0.019322
+0xa4f2
+// -0.020828
+0xa555
+// -0.055185
+0xab10
+// -0.005840
+0x9dfb
+// 0.007642
+0x1fd3
+// -0.012141
+0xa237
+// -0.005708
+0x9dd8
+// -0.073105
+0xacae
+// 0.004961
+0x1d14
+// -0.025444
+0xa684
+// 0.038032
+0x28de
+// -0.023309
+0xa5f8
+// 0.984341
+0x3be0
+// 0.057406
+0x2b59
+// 0.038917
+0x28fb
+// -0.034246
+0xa862
+// -0.030406
+0xa7c9
+// -0.015718
+0xa406
+// -0.025802
+0xa69b
+// 0.081464
+0x2d37
+// 0.026505
+0x26c9
+// 0.006918
+0x1f16
+// 0.029659
+0x2798
+// 0.091538
+0x2ddc
+// 0.065798
+0x2c36
+// 0.016095
+0x241f
+// 0.019768
+0x250f
+// 0.023790
+0x2617
+// -0.034357
+0xa866

Testing/Patterns/DSP/Bayes/BayesF16/Predicts2_s16.txt

@@ -0,0 +1,20 @@
+H
+9
+// 1
+0x0001
+// 2
+0x0002
+// 1
+0x0001
+// 2
+0x0002
+// 4
+0x0004
+// 3
+0x0003
+// 2
+0x0002
+// 1
+0x0001
+// 3
+0x0003

Testing/Patterns/DSP/Bayes/BayesF16/Probas2_f16.txt

@@ -0,0 +1,25 @@
+H
+36
+// -209.584253
+0xda8d
+// 15.746762
+0x4be0
+// -254.107146
+0xdbf1
+// -157.088915
+0xd8e9
+// -92.588131
+0xd5c9
+// 15.834292
+0x4beb
+// -127.374179
+0xd7f6
+// -242.993175
+0xdb98
+// -2791.670271
+0xe974
+// -242.605961
+0xdb95
+// 17.386646
+0x4c59
+// -5143447946.019080

Testing/Patterns/DSP/Bayes/BayesF32/DimsBench2_s16.txt

@@ -0,0 +1,38 @@
+H
+18
+// 0
+0x0000
+// 0
+0x0000
+// 12
+0x000C
+// 76
+0x004C
+// 24
+0x0018
+// 202
+0x00CA
+// 36
+0x0024
+// 303
+0x012F
+// 50
+0x0032
+// 391
+0x0187
+// 64
+0x0040
+// 537
+0x0219
+// 78
+0x004E
+// 654
+0x028E
+// 98
+0x0062
+// 778
+0x030A
+// 118
+0x0076
+// 984
+0x03D8

Testing/Patterns/DSP/Bayes/BayesF32/Inputs2_f32.txt

@@ -0,0 +1,278 @@
+W
+138
+// 0.993859
+0x3f7e6d8d
+// 0.003266
+0x3b560992
+// 0.035641
+0x3d11fcc8
+// -0.090419
+0xbdb92d74
+// 0.043912
+0x3d33dd75
+// -0.042546
+0xbd2e44f2
+// 0.070849
+0x3d911948
+// -0.065232
+0xbd859867
+// 0.007983
+0x3c02cab8
+// -0.001672
+0xbadb2eff
+// -0.028464
+0xbce92cc8
+// 0.010911
+0x3c32c31a
+// -0.092912
+0xbdbe48c5
+// 0.035668
+0x3d121905
+// -0.000463
+0xb9f2ce6a
+// 0.946335
+0x3f72430a
+// 0.007799
+0x3bff8e6c
+// -0.038055
+0xbd1bdf91
+// -0.001537
+0xbac9679b
+// -0.021848
+0xbcb2faf7
+// -0.081510
+0xbda6eeae
+// 0.013917
+0x3c6405b4
+// -0.018004
+0xbc937ce5
+// -0.018478
+0xbc975fdc
+// 0.001077
+0x3a8d3045
+// -0.069425
+0xbd8e2edc
+// 0.978310
+0x3f7a7285
+// -0.015063
+0xbc76ccc2
+// -0.100549
+0xbdcdec89
+// 0.057286
+0x3d6aa507
+// -0.028425
+0xbce8dc1e
+// 0.014849
+0x3c73499a
+// -0.050356
+0xbd4e41db
+// 0.047657
+0x3d433441
+// 0.008310
+0x3c082839
+// 0.040505
+0x3d25e83f
+// -0.008813
+0xbc106253
+// 0.045229
+0x3d39427e
+// 1.030162
+0x3f83dc5a
+// 0.015668
+0x3c805992
+// 0.017410
+0x3c8e9f6a
+// 0.008313
+0x3c083303
+// -0.006206
+0xbbcb5f18
+// 0.039141
+0x3d2051f2
+// -0.028887
+0xbceca4dd
+// 0.057686
+0x3d6c47e2
+// 0.049255
+0x3d49bff8
+// -0.031344
+0xbd006299
+// 0.096180
+0x3dc4fa04
+// 0.027573
+0x3ce1e1c8
+// 0.912040
+0x3f697b77
+// -0.006232
+0xbbcc361a
+// 0.037752
+0x3d1aa1f4
+// 0.080582
+0x3da50835
+// -0.029815
+0xbcf43f5f
+// 0.039508
+0x3d21d378
+// 0.070669
+0x3d90bb29
+// -0.023232
+0xbcbe5201
+// -0.013015
+0xbc553b5e
+// 0.045539
+0x3d3a8793
+// 0.015317
+0x3c7af242
+// -0.038341
+0xbd1d0bd8
+// -0.001877
+0xbaf5f566
+// 0.033180
+0x3d07e73d
+// -0.009663
+0xbc1e5304
+// -0.082668
+0xbda94df0
+// 0.005600
+0x3bb77c75
+// 0.895062
+0x3f6522c8
+// 0.031259
+0x3d00093a
+// -0.013758
+0xbc6168d0
+// 0.067527
+0x3d8a4b85
+// 0.058772
+0x3d70ba6a
+// -0.003151
+0xbb4e893a
+// -0.029285
+0xbcefe650
+// 0.022418
+0x3cb7a583
+// 0.015898
+0x3c823d8a
+// -0.033489
+0xbd092c45
+// -0.073212
+0xbd95efea
+// 1.016488
+0x3f821c44
+// 0.053423
+0x3d5ad1c4
+// 0.011955
+0x3c43dce2
+// 0.020540
+0x3ca84447
+// 0.017797
+0x3c91cb17
+// 0.002332
+0x3b18d194
+// 0.022311
+0x3cb6c679
+// 0.029993
+0x3cf5b3fc
+// -0.024722
+0xbcca85ac
+// -0.040891
+0xbd277dbc
+// -0.008009
+0xbc0339b6
+// 0.033292
+0x3d085d91
+// 0.021992
+0x3cb42852
+// -0.017457
+0xbc8f0267
+// 0.044975
+0x3d383751
+// 0.038378
+0x3d1d3291
+// -0.016065
+0xbc839aba
+// 0.032482
+0x3d050c11
+// 0.052955
+0x3d58e783
+// 0.040116
+0x3d2450a0
+// 0.016201
+0x3c84b8be
+// -0.025682
+0xbcd2621d
+// 1.011317
+0x3f8172d6
+// 0.007611
+0x3bf9678a
+// 0.036635
+0x3d160ed5
+// 0.041180
+0x3d28acc2
+// 0.097578
+0x3dc7d6e9
+// -0.028906
+0xbceccb92
+// -0.126198
+0xbe013a2a
+// -0.115196
+0xbdebebf8
+// 0.066253
+0x3d87afca
+// -0.000212
+0xb95ea1a9
+// -0.109596
+0xbde0739e
+// -0.029895
+0xbcf4e6bc
+// -0.043222
+0xbd310982
+// 0.067351
+0x3d89ef58
+// 0.015362
+0x3c7bb139
+// -0.032368
+0xbd04941e
+// -0.046210
+0xbd3d46b7
+// 0.078026
+0x3d9fcc50
+// -0.019191
+0xbc9d3608
+// 0.993031
+0x3f7e374a
+// -0.028901
+0xbcecc174
+// -0.034949
+0xbd0f2637
+// 0.002125
+0x3b0b3e33
+// 0.031756
+0x3d02122b
+// -0.075827
+0xbd9b4b43
+// -0.053971
+0xbd5d10ca
+// -0.014613
+0xbc6f6bc9
+// -0.052876
+0xbd589502
+// 0.023186
+0x3cbdf07b
+// 0.000434
+0x39e3664d
+// -0.028223
+0xbce73343
+// -0.021082
+0xbcacb466
+// -0.001837
+0xbaf0bf56
+// -0.005527
+0xbbb51ea2
+// -0.044851
+0xbd37b529
+// -0.069432
+0xbd8e322d
+// 0.045976
+0x3d3c5144
+// 0.004058
+0x3b84f778

Testing/Patterns/DSP/Bayes/BayesF32/Predicts2_s16.txt

@@ -0,0 +1,20 @@
+H
+9
+// 0
+0x0000
+// 3
+0x0003
+// 2
+0x0002
+// 2
+0x0002
+// 0
+0x0000
+// 3
+0x0003
+// 0
+0x0000
+// 2
+0x0002
+// 1
+0x0001

Testing/Patterns/DSP/Bayes/BayesF32/Probas2_f32.txt

@@ -0,0 +1,74 @@
+W
+36
+// -170.307298
+0xc32a4eab
+// -267.771059
+0xc385e2b2
+// 16.290787
+0x41825388
+// -234.610901
+0xc36a9c64
+// -140.670377
+0xc30cab9e
+// -287.238709
+0xc38f9e8e
+// 8.717363
+0x410b7a52
+// -249.629949
+0xc379a144
+// -169.858412
+0xc329dbc1
+// 16.384652
+0x418313c5
+// -3171.146048
+0xc5463256
+// -58.258899
+0xc269091d
+// -135.793466
+0xc307cb21
+// 18.502745
+0x4194059f
+// -151.200604
+0xc317335b
+// -219.228024
+0xc35b3a60
+// -129.752365
+0xc301c09b
+// 18.600433
+0x4194cdb0
+// -2255.153596
+0xc50cf275
+// -138.331120
+0xc30a54c4
+// -203.517142
+0xc34b8463
+// -133.665973
+0xc305aa7d
+// 19.542878
+0x419c57d1
+// -214.140087
+0xc35623dd
+// -463.633133
+0xc3e7d10b
+// -141.046419
+0xc30d0be2
+// 27.619449
+0x41dcf4a2
+// -336.431591
+0xc3a8373e
+// -328.807438
+0xc3a4675a
+// 19.551493
+0x419c6975
+// -143.817546
+0xc30fd14b
+// -580.701057
+0xc4112cde
+// 21.032212
+0x41a841f9
+// -123.852127
+0xc2f7b44a
+// -300.631048
+0xc39650c6
+// -188.210839
+0xc33c35fa

Testing/Source/Benchmarks/BayesF32.cpp

@@ -0,0 +1,79 @@
+#include "BayesF32.h"
+#include <stdio.h>
+#include "Error.h"
+#include "Test.h"
+
+
+
+    void BayesF32::test_gaussian_naive_bayes_predict_f32()
+    {
+       int16_t p;
+
+       p = arm_gaussian_naive_bayes_predict_f32(&bayes, 
+                inp, 
+                bufp);
+
+    } 
+
+  
+    void BayesF32::setUp(Testing::testID_t id,std::vector<Testing::param_t>& paramsArgs,Client::PatternMgr *mgr)
+    {
+
+       std::vector<Testing::param_t>::iterator it = paramsArgs.begin();
+       this->vecDim = *it++;
+       this->classNb = *it++;
+
+       switch(id)
+       {
+          case BayesF32::TEST_GAUSSIAN_NAIVE_BAYES_PREDICT_F32_1:
+          {
+
+            int nbp,nbi;
+
+            input.reload(BayesF32::INPUTS2_F32_ID,mgr);
+            params.reload(BayesF32::PARAMS2_F32_ID,mgr);
+            dims.reload(BayesF32::DIMS2_S16_ID,mgr);
+
+            int16_t *dimsp=dims.ptr();
+
+            nbi = dimsp[2*this->nb];
+            nbp = dimsp[2*this->nb + 1];
+
+            const float32_t *paramsp = params.ptr() + nbp;
+
+            this->theta=paramsp ;
+            this->sigma=paramsp + (this->classNb * this->vecDim);
+            this->classPrior=paramsp + 2*(this->classNb * this->vecDim);
+            this->epsilon=paramsp[this->classNb + 2*(this->classNb * this->vecDim)];
+            //printf("%f %f %f\n",this->theta[0],this->sigma[0],this->classPrior[0]);
+
+            // Reference patterns are not loaded when we are in dump mode
+            predicts.reload(BayesF32::PREDICTS2_S16_ID,mgr);
+
+            outputProbas.create(this->classNb,BayesF32::OUT_PROBA_F32_ID,mgr);
+
+            bayes.vectorDimension=this->vecDim;
+            bayes.numberOfClasses=this->classNb;
+            bayes.theta=this->theta;
+            bayes.sigma=this->sigma;
+            bayes.classPriors=this->classPrior;
+            bayes.epsilon=this->epsilon; 
+
+            this->inp = input.ptr() + nbi;
+
+            this->bufp = outputProbas.ptr();
+
+          }
+          break;
+
+       }
+       
+
+
+    }
+
+    void BayesF32::tearDown(Testing::testID_t id,Client::PatternMgr *mgr)
+    {
+        (void)id;
+        this->nb++;
+    }

Testing/bench.txt

@@ -747,6 +747,41 @@ group Root {
              }
         }
 
+        group Bayes{
+           class = Bayes
+           folder = Bayes 
+
+           suite Bayes F32 {
+              class = BayesF32
+              folder = BayesF32
+
+              ParamList {
+                  VECDIM,NBCLASSES
+                  Summary VECDIM,NBCLASSES
+                  Names "Vector dimension","Number of classes"
+                  Formula "VECDIM*NBCLASSES"
+                }
+
+              Pattern DIMS2_S16_ID : DimsBench2_s16.txt 
+              Pattern INPUTS2_F32_ID : Inputs2_f32.txt 
+              Pattern PARAMS2_F32_ID : Params2_f32.txt 
+              Pattern PREDICTS2_S16_ID : Predicts2_s16.txt
+
+              Output  OUT_PROBA_F32_ID : Probas
+              Output  OUT_PREDICT_S16_ID : Predicts
+
+              // Must be consisten with the Python script
+              Params PARAM1_ID = {
+                  VECDIM = [12,14,20]
+                  NBCLASSES = [3,5,4]
+                }
+
+              Functions {
+                 arm_gaussian_naive_bayes_predict_f32:test_gaussian_naive_bayes_predict_f32
+              } -> PARAM1_ID
+           }
+        }
+
         group Barycenter {
            class = Barycenter