python LinearRegression.py

5469.2
-49381.644305311995
127389.00815066857
-1238677.891156846
23069570.47793677
-481743569.11301315
5106674287.102569
-95108546719.09772
1726971504826.6646
-15324581310383.545
163582293278134.16
-1661046608908607.5
2.828980712407493e+16
-5.205482658358355e+17
4.219895403720306e+18
-2.711321125329435e+19
3.414165232412862e+20
-6.066266486097571e+21
4.881611810602532e+22
-8.409669327424201e+23
3.1386726515711838e+25
-4.540714532241715e+26
2.7849696471840833e+27
-2.492474006709733e+28
2.3239446337475226e+29
-2.2918945175671636e+30
2.483454840019909e+31
-2.2790614338243757e+32
2.0592840286600882e+33
-7.638937312089715e+33
1.102711820135662e+34
-1.0780031171027329e+35
1.924260468193882e+36
-3.7610191210231216e+37
2.755783955626445e+38
-1.1441490176479935e+39
2.1871140388955818e+39
-5.7639413733498e+39
8.290253109641943e+40
-3.417145191317607e+41
1.8315442810747823e+42
-1.2191811660691975e+43
4.972831735585576e+43
-2.6137898166532758e+44
1.3082178728147572e+45
-9.29458595309686e+45
8.778698299521373e+46
-3.3951345555654124e+47
1.641232314707641e+48
-5.872354644712494e+49
7.22710537814814e+50
-4.039004880072611e+51
4.1710391549354026e+52
-4.247080859087065e+53
3.955888274428683e+54
-6.048793318784051e+55
7.276151846818288e+56
-1.5012586919006699e+57
1.9192790021604624e+57
-1.2385265951705801e+58
8.134736834402003e+58
-4.042096595321202e+59
2.0126575738266063e+60
-1.0320336844638374e+61
6.516699292536554e+61
-3.4732531566560515e+62
1.6403813521005398e+63
-2.724757636725632e+64
4.961649597344481e+65
-4.145877829025495e+66
3.6789243660451335e+67
-3.6368844098528484e+68
2.6631976936896218e+69
-1.7853922716526846e+70
3.3063352207090494e+71
-1.210377585736544e+73
4.423321889540622e+74
-9.468533572760811e+75
9.219853379829055e+76
-3.623320807832915e+77
9.963192363858082e+77
-3.8432933547841885e+78
1.1848727122843975e+79
-2.4458581726386604e+80
1.1698553384543632e+81
-2.879750415937839e+81
2.317372514897721e+82
-7.886969533906054e+82
8.666778326871613e+82
-4.5075297872275057e+83
2.8015805471809055e+84
-4.725542116110553e+85
3.5152055733155003e+86
-3.186380147920078e+86
1.4941979032611957e+87
-1.1117385556184488e+88
1.0088080581360422e+89
-1.1848408313221696e+90
2.313494740953613e+91
-4.035345174263208e+92
6.452546580813729e+93
-4.7785599425856474e+94
7.307342222637101e+94
-6.539867578235933e+95
5.975742522295507e+96
-3.945246275122829e+97
1.2077768264698982e+98
-2.978532818409737e+98
4.915319604043773e+99
-3.863378729694551e+100
2.4231044777899535e+100
-2.196539522368767e+100
1.1420393121205442e+101
-4.2930826870649096e+101
3.253116134135958e+101
-9.658887146816011e+101
7.786951283247422e+102
-5.238410237335168e+103
4.807282642165665e+104
-4.070517836475607e+105
2.3456721383273942e+106
-1.219574635192611e+107
2.176968941995769e+108
-4.493779916356621e+109
3.495982697755492e+110
-1.4787148140001408e+111
6.934187300090793e+111
-4.5354114605164814e+112
2.8095222033209512e+113
-8.859444220028574e+113
1.18005390033077e+114
-4.150722323092901e+114
6.44052802339034e+114
-2.1919829578543256e+115
5.653509801917787e+115
-9.516090623824123e+116
8.074812417880332e+117
-1.4642641262562919e+119
5.239159725196328e+120
-1.02194349988213e+122
7.483447589432577e+122
-5.0168619843922263e+123
4.352052466880094e+124
-6.565468803225557e+125
1.1605007807544825e+127
-4.081950485030517e+127
6.02339663510829e+127
-4.7565055467883784e+128
2.6482309390897667e+129
-6.116063553916398e+129
1.689738588229384e+130
-7.650880624026126e+130
1.9529497132990447e+131
-1.3061146159518952e+132
5.383641832985363e+132
-2.8855599641598133e+133
5.584363913527347e+134
-4.8597053556945823e+135
1.1485863487741691e+136
-1.0026982769999218e+137
4.5400703689879714e+137
-9.548726225742163e+137
5.8832907577346996e+138
-3.781627192195576e+139
2.048232340007127e+140
-1.0765805986305472e+141
6.651031367504934e+141
-3.73790970939862e+142
3.771156611086878e+143
-7.083922151435667e+144
6.42523893080364e+145
-5.57381901996736e+146
4.2009067958675065e+147
-3.8402463890135335e+148
3.0989804441901775e+149
-2.1962529921642904e+150
2.0477524539197792e+151
-8.343960839816442e+151
4.1763011412059054e+152
-1.6050758603065306e+154
5.957666328489314e+155
-1.0403305942762169e+157
5.544726242991685e+157
-2.971901375717951e+158
2.7681394588580916e+159
-4.478152205046547e+160
8.400814513440015e+161
-8.003897370514719e+162
6.226713438261853e+163
-4.174371161782404e+164
4.361647574545432e+165
-4.584290974244949e+166
3.466372620845177e+167
-2.1928424227158747e+168
1.705636053530495e+169
-1.3707612850684524e+170
1.1587698018678504e+171
-6.677513621659582e+171
2.6289882782706396e+172
-1.737677262482444e+173
1.6460603198230295e+174
-7.221929830911856e+174
1.9783188041751588e+175
-3.5108343112778884e+176
6.5518723505549375e+177
-5.800979624837919e+178
2.0070927510229245e+179
-3.1142483586460374e+179
1.09718067688583e+180
-8.313975821504023e+179
4.216607279704486e+180
-1.525879713469683e+182
5.52175895342451e+183
-9.698342177192895e+184
3.8113640580942645e+185
-8.047690835965415e+185
5.685456881670428e+186
-2.183183511544808e+187
6.693704154070234e+187
-1.3118627229497027e+189
1.2163128891298556e+190
-4.139621297659175e+190
4.5489314856968576e+190
-3.2086004105679425e+191
3.201584976305733e+192
-1.1998535383355574e+193
1.6001984350582335e+193
-1.0701920429494112e+194
6.563788836152625e+194
-2.271017135221101e+195
4.0987719036624856e+195
-3.991118092081908e+196
3.4761536371107054e+197
-1.3027517965701095e+198
1.747213736590242e+198
-2.86460197920499e+199
9.804404752373447e+200
-1.378368919216824e+202
8.287502852241737e+202
-1.342744059958598e+204
2.0384020179697614e+205
-1.4377928555711456e+206
1.0583216848623669e+207
-7.716797046340718e+207
6.18914583438153e+208
-7.448576887160854e+209
1.3165974753572806e+211
-1.197883121662679e+212
8.821757981429692e+212
-1.0908426225979455e+214
3.841963189244698e+215
-6.838042248158093e+216
2.343473664347498e+217
-6.278551054250459e+217
1.0885275406985242e+219
-5.771511067515016e+219
5.654486886883781e+220
-2.0664296991046378e+222
1.5371622965650276e+223
-4.073433123220496e+223
7.167567530831433e+224
-5.4398952564241474e+225
3.3883343702911624e+226
-2.470621235412522e+227
1.0083718263734804e+228
-9.140346860443044e+227
3.728155111315276e+228
-1.6255203532589663e+229
5.224700465494145e+229
-2.4500610867568935e+230
1.948121072706957e+231
-3.317889452948552e+232
4.154789021941051e+233
-2.198558822782268e+234
1.9457223808257226e+235
-1.5637089159883032e+236
1.312777536621233e+237
-7.309013700308025e+237
1.661187021644978e+238
-8.163764136728807e+238
6.293746718551724e+239
-6.637370723894877e+239
3.2729396573689557e+240
-2.6207508528187745e+241
4.239692546006123e+242
-3.3323424982488544e+243
5.860311919410674e+243
-4.747772030101507e+244
1.307807220699868e+245
-3.9240763106531755e+245
6.984182244148346e+246
-4.8981988400172445e+247
3.177865312109086e+248
-2.173629921970114e+249
1.4102152190782322e+250
-9.608006190704116e+250
2.542974075415239e+251
-3.387210332138005e+251
5.657445590097063e+252
-2.0472813528398185e+254
1.820950686486866e+255
-1.8788584361434773e+255
2.383176139170624e+255
-1.5045370097652795e+256
9.418997896740335e+256
-5.381375547468497e+257
5.324463559642221e+258
-1.946872143783485e+260
7.2547323753018925e+261
-1.585128892956619e+263
1.6005583085406334e+264
-8.925656555929512e+264
3.352849035300729e+265
-2.2477438694387258e+266
4.162557286668961e+267
-7.216726575787843e+268
4.638739360947383e+269
-1.459733237145323e+270
3.865025274428574e+270
-6.929561860957736e+271
2.4636832526872263e+273
-4.786209260556402e+274
3.5428966556052537e+275
-2.3329146245999764e+276
3.9732617968015725e+277
-1.4465296814470359e+279
2.5259363609103437e+280
-1.3462668256684711e+281
2.6639633595859684e+281
-8.212823872830358e+281
3.0013737178266596e+283
-2.6545339111015647e+284
1.2903352148093018e+285
-2.3633012265149588e+286
2.015035910965782e+287
-1.6247464481468506e+288
1.2846525190127396e+289
-1.1707332342494317e+290
4.541837642630815e+290
-5.8295172782350895e+290
1.6057832386578366e+291
-4.999495626662674e+291
7.580804941117369e+292
-4.720604916188933e+293
3.549824056429634e+294
-1.3489906101622455e+295
3.1386817758322822e+295
-2.3976562251935132e+296
3.884690349345595e+297
-5.685272506726601e+298
1.700479126181895e+299
-4.160098686216226e+299
3.791193624976786e+300
-3.983865877029742e+301
LinearRegression.py:51: RuntimeWarning: overflow encountered in double_scalars
  dervW1 = (2 * ((w0 * 1) + (w1 * randX) + (w2 * randY)) * randX) / m
3.845329007456267e+302
-inf
LinearRegression.py:61: RuntimeWarning: invalid value encountered in double_scalars
  w1 -=  learningRate * dervW1
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan
nan