conv
用途：conv函数用于一维卷积。
类型：普通函数
隶属：Signal & Image Processing
- 1D convolution of vectors A and B
- The orientation of the result vector is the same as the orientation of A (ie. either column or row vector)
- The shape argument is optional; it is one of:
“full” = return the full convolution (default setting), with the size equal toA.n_elem + B.n_elem - 1
“same” = return the central part of the convolution, with the same size as vector A - The convolution operation is also equivalent to FIR filtering
示例代码如下：
vec A(256, fill::randu);
vec B(16, fill::randu);
vec C = conv(A, B);
vec D = conv(A, B, "same");运行结果：
Armadillo version: 9.900.1 (Nocturnal Misbehaviour)
A:
0.0013
0.1933
0.5850
0.3503
0.8228
0.1741
0.7105
0.3040
0.0914
0.1473
0.9885
0.1191
0.0089
0.5317
0.6018
0.1662
0.4508
0.0571
0.7833
0.5199
0.8760
0.9559
0.5393
0.4621
0.8622
0.7797
0.9968
0.6115
0.2662
0.8401
0.3759
0.6772
0.0088
0.2759
0.5879
0.8376
0.4849
0.7437
0.4580
0.7444
0.5990
0.7350
0.5724
0.1516
0.4252
0.5171
0.7515
0.1690
0.4919
0.6998
0.1475
0.1416
0.6929
0.4265
0.9666
0.1533
0.8217
0.1914
0.8172
0.1556
0.7320
0.2796
0.6822
0.7219
0.1230
0.8347
0.5170
0.4262
0.9493
0.5495
0.4717
0.8470
0.4561
0.9829
0.7392
0.1960
0.8394
0.5009
0.0275
0.5726
0.5313
0.8430
0.6576
0.8421
0.1099
0.3141
0.2861
0.1403
0.8346
0.6002
0.2527
0.0016
0.8062
0.2106
0.5532
0.1138
0.7522
0.5434
0.4367
0.6962
0.4367
0.5779
0.6287
0.5041
0.6958
0.1900
0.1784
0.4575
0.0975
0.0944
0.9315
0.8946
0.2273
0.4107
0.6281
0.4516
0.5978
0.8548
0.6248
0.5657
0.1843
0.5551
0.2429
0.6047
0.5846
0.4944
0.7407
0.6204
0.8045
0.5760
0.9115
0.7277
0.6678
0.3150
0.3058
0.1086
0.8512
0.1549
0.0793
0.6410
0.5451
0.4090
0.4656
0.1527
0.7380
0.8267
0.8733
0.3001
0.1272
0.7850
0.6096
0.0723
0.6538
0.1048
0.2271
0.9200
0.6628
0.4925
0.4968
0.5093
0.6882
0.6063
0.0059
0.1006
0.8632
0.7473
0.3801
0.5528
0.9557
0.1766
0.1316
0.9517
0.0278
0.0559
0.1316
0.8643
0.7216
0.0147
0.7079
0.2176
0.1689
0.3410
0.3676
0.8024
0.5265
0.7982
0.1448
0.4024
0.1366
0.0662
0.5733
0.5480
0.3113
0.8080
0.2843
0.8952
0.7438
0.3611
0.2285
0.2294
0.5424
0.0538
0.5247
0.0947
0.8915
0.1465
0.9316
0.0471
0.3364
0.3986
0.9462
0.5342
0.6935
0.2592
0.5256
0.3987
0.5855
0.6840
0.4355
0.0072
0.6016
0.5767
0.2223
0.0043
0.0823
0.8551
0.8111
0.6915
0.5301
0.1428
0.7279
0.0311
0.6445
0.0855
0.9479
0.2750
0.9818
0.2923
0.3675
0.2186
0.2406
0.9023
0.9026
0.0801
0.1718
0.7759
0.2106
0.0038
0.1141
0.3112
0.0386
0.1893
0.1535
0.8854
0.1957
0.6456
B:
0.9091
0.9213
0.1508
0.5695
0.9430
0.5786
0.2553
0.3503
0.7952
0.0703
0.5907
0.1977
0.6443
0.6043
0.4413
0.3965
C:
0.0012
0.1769
0.7101
0.8873
1.2703
1.4854
1.7938
2.1444
1.7661
1.7571
2.8044
2.5578
1.9188
2.2350
3.8802
2.7669
2.9627
3.1577
3.8220
3.4011
3.5408
4.0236
4.1757
3.9416
4.5852
4.7820
5.1713
4.6817
5.0271
5.1147
5.1975
5.0501
4.7720
4.7721
5.2644
5.0937
4.6981
4.8401
4.9184
5.4867
4.6445
5.0565
4.7270
4.4557
4.1643
4.4683
4.3752
4.2173
4.0366
5.0476
4.4173
3.5157
4.2808
4.3718
4.4732
3.6408
4.1538
4.1189
4.0501
4.0399
4.0429
4.0724
4.2595
3.9630
4.2891
3.6887
5.1367
4.0654
4.9462
4.6124
4.6667
4.7216
4.7542
5.3165
4.9616
4.9953
5.0308
5.3105
4.6404
4.3458
4.9759
5.3531
4.6972
5.1073
5.0139
4.3356
4.6888
4.1837
4.1174
4.5649
3.5408
3.4751
3.7665
4.2761
3.5429
3.5590
4.1369
3.8499
3.4463
3.2932
4.1865
3.7933
4.4052
3.8475
4.4819
3.7614
3.7042
4.0190
3.5985
3.3389
3.7644
4.6914
3.8784
3.3116
4.5002
4.3804
3.8032
4.2292
4.5362
4.1472
3.7049
4.0504
4.1979
4.1953
4.4963
4.4899
4.4642
4.6299
4.6764
5.0968
4.9946
5.6097
5.0588
4.7630
4.5852
4.2582
4.8728
4.3666
3.9381
4.1971
4.8910
4.1215
4.0072
3.9467
4.5022
4.2088
4.3621
3.7647
3.4710
4.3233
4.4224
3.4463
3.7946
4.0509
3.8700
3.8102
4.3700
4.2492
4.1193
4.5949
4.7191
4.0612
3.8334
3.4564
4.2585
4.5301
3.4619
3.8486
5.0459
4.7163
3.6173
4.4750
4.5850
3.2325
3.1418
3.9721
4.2076
2.9006
3.6634
4.4958
3.3186
3.3204
3.1064
3.9718
3.3832
3.3879
3.7361
3.0813
3.7836
3.1507
3.4311
3.7250
2.9084
3.6424
3.3613
4.1506
4.2860
4.2542
3.9165
3.6712
3.8691
3.2741
2.8713
3.6415
3.6297
3.8733
3.7647
3.8150
3.7414
3.5235
4.3645
3.5336
3.8944
3.2960
4.3481
3.5342
4.2054
4.0698
4.4595
3.6384
3.9578
4.2051
3.8929
3.1462
3.5636
4.1482
4.0717
3.5623
3.8914
3.8875
4.2337
3.6712
3.3787
3.6359
3.7165
3.9342
3.6998
3.8782
4.1428
3.8065
4.4282
3.8447
4.8854
3.2111
3.6183
3.9625
4.3953
2.9344
3.0682
3.8912
2.9828
2.2922
2.3513
3.2548
3.0304
2.9091
2.7856
2.4907
2.0028
1.9016
1.4296
1.4223
0.8368
1.4083
0.6897
1.1821
0.9315
0.9858
0.8275
0.3625
0.2560
D:
1.7661
1.7571
2.8044
2.5578
1.9188
2.2350
3.8802
2.7669
2.9627
3.1577
3.8220
3.4011
3.5408
4.0236
4.1757
3.9416
4.5852
4.7820
5.1713
4.6817
5.0271
5.1147
5.1975
5.0501
4.7720
4.7721
5.2644
5.0937
4.6981
4.8401
4.9184
5.4867
4.6445
5.0565
4.7270
4.4557
4.1643
4.4683
4.3752
4.2173
4.0366
5.0476
4.4173
3.5157
4.2808
4.3718
4.4732
3.6408
4.1538
4.1189
4.0501
4.0399
4.0429
4.0724
4.2595
3.9630
4.2891
3.6887
5.1367
4.0654
4.9462
4.6124
4.6667
4.7216
4.7542
5.3165
4.9616
4.9953
5.0308
5.3105
4.6404
4.3458
4.9759
5.3531
4.6972
5.1073
5.0139
4.3356
4.6888
4.1837
4.1174
4.5649
3.5408
3.4751
3.7665
4.2761
3.5429
3.5590
4.1369
3.8499
3.4463
3.2932
4.1865
3.7933
4.4052
3.8475
4.4819
3.7614
3.7042
4.0190
3.5985
3.3389
3.7644
4.6914
3.8784
3.3116
4.5002
4.3804
3.8032
4.2292
4.5362
4.1472
3.7049
4.0504
4.1979
4.1953
4.4963
4.4899
4.4642
4.6299
4.6764
5.0968
4.9946
5.6097
5.0588
4.7630
4.5852
4.2582
4.8728
4.3666
3.9381
4.1971
4.8910
4.1215
4.0072
3.9467
4.5022
4.2088
4.3621
3.7647
3.4710
4.3233
4.4224
3.4463
3.7946
4.0509
3.8700
3.8102
4.3700
4.2492
4.1193
4.5949
4.7191
4.0612
3.8334
3.4564
4.2585
4.5301
3.4619
3.8486
5.0459
4.7163
3.6173
4.4750
4.5850
3.2325
3.1418
3.9721
4.2076
2.9006
3.6634
4.4958
3.3186
3.3204
3.1064
3.9718
3.3832
3.3879
3.7361
3.0813
3.7836
3.1507
3.4311
3.7250
2.9084
3.6424
3.3613
4.1506
4.2860
4.2542
3.9165
3.6712
3.8691
3.2741
2.8713
3.6415
3.6297
3.8733
3.7647
3.8150
3.7414
3.5235
4.3645
3.5336
3.8944
3.2960
4.3481
3.5342
4.2054
4.0698
4.4595
3.6384
3.9578
4.2051
3.8929
3.1462
3.5636
4.1482
4.0717
3.5623
3.8914
3.8875
4.2337
3.6712
3.3787
3.6359
3.7165
3.9342
3.6998
3.8782
4.1428
3.8065
4.4282
3.8447
4.8854
3.2111
3.6183
3.9625
4.3953
2.9344
3.0682
3.8912
2.9828
2.2922
2.3513
3.2548
3.0304
2.9091
2.7856
2.4907
2.0028
1.9016
1.4296
1.4223
0.8368
1.4083
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