*************How to reduce dimentionality on Sparse Matrix in Python**************
[[ 0. -0.33501649 -0.04308102 ... -1.14664746 -0.5056698
-0.19600752]
[ 0. -0.33501649 -1.09493684 ... 0.54856067 -0.5056698
-0.19600752]
[ 0. -0.33501649 -1.09493684 ... 1.56568555 1.6951369
-0.19600752]
...
[ 0. -0.33501649 -0.88456568 ... -0.12952258 -0.5056698
-0.19600752]
[ 0. -0.33501649 -0.67419451 ... 0.8876023 -0.5056698
-0.19600752]
[ 0. -0.33501649 1.00877481 ... 0.8876023 -0.26113572
-0.19600752]]
(0, 1) -0.3350164872543856
(0, 2) -0.04308101770538793
(0, 3) 0.2740715207154218
(0, 4) -0.6644775126361527
(0, 5) -0.8441293865949171
(0, 6) -0.40972392088346243
(0, 7) -0.1250229232970408
(0, 8) -0.05907755711884675
(0, 9) -0.6240092623290964
(0, 10) 0.4829744992519545
(0, 11) 0.7596224512649244
(0, 12) -0.05842586308220443
(0, 13) 1.1277211297338117
(0, 14) 0.8795830595483867
(0, 15) -0.13043338063115095
(0, 16) -0.04462507326885248
(0, 17) 0.11144272449970435
(0, 18) 0.8958804382797294
(0, 19) -0.8606663175537699
(0, 20) -1.1496484601880896
(0, 21) 0.5154718747277965
(0, 22) 1.905963466976408
(0, 23) -0.11422184388584329
(0, 24) -0.03337972630405602
(0, 25) 0.48648927722411006
: :
(1796, 38) -0.8226945146290309
(1796, 40) -0.061343668908253476
(1796, 41) 0.8105536026095989
(1796, 42) 1.3950951873625397
(1796, 43) -0.19072005925701047
(1796, 44) -0.5868275383619802
(1796, 45) 1.3634658076459107
(1796, 46) 0.5874903313016945
(1796, 47) -0.08874161717060432
(1796, 48) -0.035433262605025426
(1796, 49) 4.179200682513991
(1796, 50) 1.505078217025183
(1796, 51) 0.0881769306516768
(1796, 52) -0.26718796251356636
(1796, 53) 1.2010187221077009
(1796, 54) 0.8692294429227895
(1796, 55) -0.2097851269640334
(1796, 56) -0.023596458909150665
(1796, 57) 0.7715345500122912
(1796, 58) 0.47875261517372414
(1796, 59) -0.020358468129093202
(1796, 60) 0.4441643511677691
(1796, 61) 0.8876022965425754
(1796, 62) -0.26113572420685327
(1796, 63) -0.1960075186604789
[[ 1.9142142 -0.95450588 -3.94605003 ... 1.48605508 0.1580507
-0.81430216]
[ 0.58898116 0.92463167 3.92473516 ... 0.55387834 1.07380158
0.11517957]
[ 1.3020402 -0.31720719 3.02328596 ... 1.10558107 0.86551794
-0.91136424]
...
[ 1.02259475 -0.1478891 2.47003035 ... 0.55809882 2.09899726
-2.06791444]
[ 1.07605326 -0.38087334 -2.45539167 ... 0.82143985 1.04432596
-0.44841053]
[-1.25770332 -2.22756573 0.28369586 ... -1.17449788 0.8603674
-1.87848472]]
Original number of features: 64
Reduced number of features: 10
0.456120019803888