7:00 – 5:00 ASRC NASA GOES-R
- Create spreadsheets for tasks and bugs
- More dissertation. Add Axelrod
- Mission Drive today, no meeting with Wayne, I think
- Good visualization tool finds this morning:
- Matrix Class
- Change test and train within the class to input and target
- Create the test and train as an output with the rectangular matrix with masks included. This means that I have to re-assemble that matrix from the input and target matrices
- I still like the idea of persisting these values as excel spreadsheets
- And now a pile of numbers that makes me happy:
MatrixScalar: rows = 5 input_size = 5 target_size = 5 mask_value(hex) = -1 tmax_cols = 6 mat_min = 0.13042279608514273 mat_max = 9.566827711787509 input_npmat = [4.384998306058251, 6.006494724381491, 7.061283542583833, 7.817876758859971, 7.214499436254831] [0.15061642402352393, 2.818956354589415, 5.04113793598655, 6.31250083574919] [2.8702355283795837, 5.564035171373476, 7.81403258383623, 8.590265450278785, 9.566827711787509] [0.1359688602006689, 0.8005043254115471, 2.080391037187722, 1.9828746089685887, 2.4669996344853677] [0.33676501126574077] target_npmat = [6.529725859535821, 4.8702784287160075, 3.677355933557321, 1.5184287945320327, -0.5429800453619322] [7.629655798004273, 8.043579124885415, 7.261429015491849, 7.137935661381686, 5.583232751491164] [8.997538924797388, 8.32502866049641, 6.5215023090524085, 4.725363596736856, 1.3761131232325439] [2.270623038824647, 2.430147101210101, 2.0903103552937132, 1.6846416494136842, 1.4289540998497225] [1.897999998722116, 1.9054555934093833, 2.883358420829866, 3.703791108487346, 4.011103843736698] scaled_input_npmat = [0.5608937619909073, 0.7683025595887466, 0.9032226729055693, 0.9999999999999999, 0.9228208193584869] [0.023860024409113324, 0.44656728417761093, 0.798596002940291, 1.0] [0.30001956916639155, 0.5815966733171017, 0.8167840813322457, 0.8979220394754807, 1.0] [0.055115071076624986, 0.32448497933342324, 0.8432879389630332, 0.8037595876588889, 1.0] [1.0] scaled_target_npmat = [0.8352300836842569, 0.6229668973991612, 0.47037783364770835, 0.1942252150254483, -0.06945364606145459] [1.2086581842168989, 1.2742301877146247, 1.1503252362944103, 1.1307619352630993, 0.88447239798718] [0.9404934630223677, 0.8701974062142825, 0.6816786614665502, 0.4939321308059752, 0.143842155904721] [0.9203986117729256, 0.9850618002693972, 0.847308741385268, 0.6828706522143898, 0.5792275279958895] [5.635977418165948, 5.658116281877606, 8.561929904750741, 10.998147030080538, 11.910690569251393] scaled, masked input = [[ 0.56089376 0.76830256 0.90322267 1. 0.92282082] [-1. 0.02386002 0.44656728 0.798596 1. ] [ 0.30001957 0.58159667 0.81678408 0.89792204 1. ] [ 0.05511507 0.32448498 0.84328794 0.80375959 1. ] [-1. -1. -1. -1. 1. ]] scaled target = [0.8352300836842569, 0.6229668973991612, 0.47037783364770835, 0.1942252150254483, -0.06945364606145459] [1.2086581842168989, 1.2742301877146247, 1.1503252362944103, 1.1307619352630993, 0.88447239798718] [0.9404934630223677, 0.8701974062142825, 0.6816786614665502, 0.4939321308059752, 0.143842155904721] [0.9203986117729256, 0.9850618002693972, 0.847308741385268, 0.6828706522143898, 0.5792275279958895] [5.635977418165948, 5.658116281877606, 8.561929904750741, 10.998147030080538, 11.910690569251393] scaled = [ 6.52972586 4.87027843 3.67735593 1.51842879 -0.54298005], error = 0.0 scaled = [7.6296558 8.04357912 7.26142902 7.13793566 5.58323275], error = 0.0 scaled = [8.99753892 8.32502866 6.52150231 4.7253636 1.37611312], error = 0.0 scaled = [2.27062304 2.4301471 2.09031036 1.68464165 1.4289541 ], error = 0.0 scaled = [1.898 1.90545559 2.88335842 3.70379111 4.01110384], error = 0.0 input_train = [[ 0.05511507 0.32448498 0.84328794 0.80375959 1. ] [-1. -1. -1. -1. 1. ] [ 0.30001957 0.58159667 0.81678408 0.89792204 1. ]] input_test = [[ 0.56089376 0.76830256 0.90322267 1. 0.92282082] [-1. 0.02386002 0.44656728 0.798596 1. ]] target_train = [[ 0.92039861 0.9850618 0.84730874 0.68287065 0.57922753] [ 5.63597742 5.65811628 8.5619299 10.99814703 11.91069057] [ 0.94049346 0.87019741 0.68167866 0.49393213 0.14384216]] target_test = [[ 0.83523008 0.6229669 0.47037783 0.19422522 -0.06945365] [ 1.20865818 1.27423019 1.15032524 1.13076194 0.8844724 ]]