Wed Oct  8 08:45:11 2025

perceptron_test():
  python version: 3.10.12
  numpy version:  1.26.4
  Use the perceptron algorithm to determine a classifier
  for sets of data.

dividing_line_test():
  Apply the perceptron to an artificial data set.
  Points (x,y) are in set 0 if y < 3x + 2
  Points (x,y) are in set 1 if y > 3x + 2
  Generate 25 points at random and search for a classifier
  using the perceptron algorithm

  Number of training data values =  25

  Training data:
[[ 0.          0.86814227  2.23100751  0.        ]
 [ 1.          0.31714063  2.97319973  1.        ]
 [ 2.          0.95389327  4.55477559  0.        ]
 [ 3.          0.82160191  3.33925972  0.        ]
 [ 4.          0.78820232  3.77668577  0.        ]
 [ 5.          0.66600506  3.18074217  0.        ]
 [ 6.          0.93284637  2.86397407  0.        ]
 [ 7.          0.98011405  4.91767029  0.        ]
 [ 8.          0.4971054   2.74322088  0.        ]
 [ 9.          0.1032757   4.91401861  1.        ]
 [10.          0.97255306  2.41730742  0.        ]
 [11.          0.69190975  2.83199354  0.        ]
 [12.          0.13882607  2.01883014  0.        ]
 [13.          0.85380568  4.40163591  0.        ]
 [14.          0.71195083  3.37550232  0.        ]
 [15.          0.48813961  3.9514309   1.        ]
 [16.          0.40336446  2.43749345  0.        ]
 [17.          0.6913014   3.98577676  0.        ]
 [18.          0.9610687   3.27145083  0.        ]
 [19.          0.91232136  2.01490951  0.        ]
 [20.          0.11471289  3.79267964  1.        ]
 [21.          0.70661408  3.84182453  0.        ]
 [22.          0.92262046  2.27041796  0.        ]
 [23.          0.23975697  2.82838771  1.        ]
 [24.          0.81580282  2.05473336  0.        ]]
  25 good y, and 25 bad y
  0.103276 <= X <= 0.980114
  2.01491 <= Y <= 4.91767
  GOOD: mean X = 0.252605, mean Y = 3.69194
  BAD:  mean X = 0.764502, mean Y = 3.12646
  Graphics saved as "dividing_line_data.png"
  Using learning rate alpha = 0.05
  Using  1000 steps
  All training data classified on step 46

  Perceptron weights:
  f(x) = -0.302601 + -0.930307 * x + 0.20727 * y

  Index    x*w  (0<x*w)  Rating

      0   -0.65      0      0
      1    0.02      1      1
      2   -0.25      0      0
      3   -0.37      0      0
      4   -0.25      0      0
      5   -0.26      0      0
      6   -0.58      0      0
      7   -0.20      0      0
      8   -0.20      0      0
      9    0.62      1      1
     10   -0.71      0      0
     11   -0.36      0      0
     12   -0.01      0      0
     13   -0.18      0      0
     14   -0.27      0      0
     15    0.06      1      1
     16   -0.17      0      0
     17   -0.12      0      0
     18   -0.52      0      0
     19   -0.73      0      0
     20    0.38      1      1
     21   -0.16      0      0
     22   -0.69      0      0
     23    0.06      1      1
     24   -0.64      0      0
  Graphics saved as "dividing_line_classified.png"

generator_ratings_test():
  Apply the perceptron to generator ratings data.
  Data file is " generators.txt "
  Data is index, rpm, vibration, rating
  Rating is 0 for bad, 1 for good.
  Use the perceptron algorithm to determine a
  classifier f(rpm,vibration) -> {0, 1}

  Generator ratings

  Number of generators = 56
  56 good generators, and 56 bad generators
  562 <= RPM <= 939
  79 <= VIB <= 585
  GOOD: mean RPM = 740.75, mean VIB = 447.286
  BAD:  mean RPM = 741.964, mean VIB = 219.857
  Graphics saved as "generator_ratings_data.png"
  All training data classified on step 8

  Perceptron weights:
  f(x) = -0.541674 + 0.397848 * r + 0.740477 * v


  Index    x*w  (0<x*w)  Rating

      0   -0.21      0      0
      1    0.07      1      1
      2    0.07      1      1
      3    0.06      1      1
      4    0.12      1      1
      5   -0.01      0      0
      6   -0.29      0      0
      7   -0.23      0      0
      8    0.10      1      1
      9   -0.02      0      0
     10   -0.05      0      0
     11    0.21      1      1
     12   -0.11      0      0
     13    0.18      1      1
     14   -0.21      0      0
     15   -0.23      0      0
     16    0.17      1      1
     17    0.23      1      1
     18   -0.22      0      0
     19    0.24      1      1
     20    0.23      1      1
     21    0.20      1      1
     22   -0.11      0      0
     23   -0.21      0      0
     24    0.14      1      1
     25    0.12      1      1
     26   -0.10      0      0
     27   -0.09      0      0
     28    0.12      1      1
     29   -0.06      0      0
     30    0.20      1      1
     31   -0.17      0      0
     32   -0.11      0      0
     33    0.20      1      1
     34   -0.08      0      0
     35   -0.23      0      0
     36    0.24      1      1
     37    0.29      1      1
     38   -0.12      0      0
     39   -0.21      0      0
     40    0.30      1      1
     41   -0.08      0      0
     42   -0.13      0      0
     43   -0.16      0      0
     44    0.10      1      1
     45    0.21      1      1
     46   -0.21      0      0
     47   -0.10      0      0
     48    0.26      1      1
     49    0.22      1      1
     50    0.17      1      1
     51    0.28      1      1
     52    0.28      1      1
     53   -0.14      0      0
     54    0.19      1      1
     55   -0.18      0      0
  Graphics saved as "generator_ratings_classified.png"

perceptron_test():
  Normal end of execution.
Wed Oct  8 08:45:12 2025