Neural network to work out cashflow and timeflow

The following neural network in Python was trained on available cashflow in my bank account prior to the time of purchase of a coffee and snack at a set location. The time spent at this activity at the cafe was measured using a stopwatch. To normalise the data used in training, the available balances were divided by a 1000 and minutes were divided by 100 so all the values fell between 0 and 1. So after running the network, the predicted output for cashflow is multiplied by 1000 and the minutes multiplied by 100. Predicted X for this example is 0.369 * 1000 = £369 predicted available cashflow on approach to the cafe, and the predicted Y  is  0.42 * 100 = 42 minutes predicted time spent there.

After saving the code to a file filename.py it can be run from a terminal by typing python filename.py   

Here is the code:


import numpy as np
from numpy import  array
from sklearn import svm

clf = svm.SVC(gamma=0.001, C=100)

y = np.array([0.381, 0.96, 0.385, 0.369, 0.3225, 0.28, 0.2776, 0.2641, 0.3415,
0.6881, 0.4925, 0.5263, 0.2965, 0.8622, 0.4678, 0.3493, 0.3008, 0.2553, 0.178,
0.3826, 0.3378, 0.4217, 0.42, 0.4197, 0.3938, 0.5988, 0.5358]).T

y = np.array(y, ndmin = 1).T
print(y, y.shape)

x = np.array([0.14569, 0.17944, 0.15496, 0.07607, 0.01223, 0.00873, 0.26456, 0.19928, 0.01417, 0.00220, 0.16451, 0.09408, 0.23073, 0.13403, 0.11177, 0.29657, 0.21266,
0.11302, 0.29185, 0.24873, 0.15997, 0.09582, 0.30616, 0.11861, 0.18292, 0.12121, 0.08206 ]).T

x = np.array(x, ndmin = 2).T
print(x, x.shape)  

xtrain, ytrain =  x[:-1], y[:-1]  

clf.fit(xtrain, ytrain) #train the data

print('Prediction for x:', clf.predict(x[-1])) #predict the data
print('Prediction for y:', clf.predict(y[-1])) #predict the data



Here is the screen / terminal output:

david@debian:~/Timecfnn$ python cashflowtime+time.py
(array([ 0.381 ,  0.96  ,  0.385 ,  0.369 ,  0.3225,  0.28  ,  0.2776,
        0.2641,  0.3415,  0.6881,  0.4925,  0.5263,  0.2965,  0.8622,
        0.4678,  0.3493,  0.3008,  0.2553,  0.178 ,  0.3826,  0.3378,
        0.4217,  0.42  ,  0.4197,  0.3938,  0.5988,  0.5358]), (27,))
(array([[ 0.14569],
       [ 0.17944],
       [ 0.15496],
       [ 0.07607],
       [ 0.01223],
       [ 0.00873],
       [ 0.26456],
       [ 0.19928],
       [ 0.01417],
       [ 0.0022 ],
       [ 0.16451],
       [ 0.09408],
       [ 0.23073],
       [ 0.13403],
       [ 0.11177],
       [ 0.29657],
       [ 0.21266],
       [ 0.11302],
       [ 0.29185],
       [ 0.24873],
       [ 0.15997],
       [ 0.09582],
       [ 0.30616],
       [ 0.11861],
       [ 0.18292],
       [ 0.12121],
       [ 0.08206]]), (27, 1))
('Prediction for x:', array([ 0.369]))
('Prediction for y:', array([ 0.42]))
david@debian:~/Timecfnn$


Acknowledgements: I  would like to thank Jack's Snax of Barum Arcade in Barnstaple for the endless supply of coffee, and houmous and haloumi wraps with lettuce and tomato and pleasant conversation.

Picture: me outside Jack's Snax