Imperial College of Science, Technology and Medicine Modelling Neural Processing Using Venn-Networks in Physiological and Pathological Scenarios PhD Thesis – Fernando Buarque de Lima Neto, BSc MSc London/United Kingdom, August 2002

Training and testing data

Data quest

An artificial neural network is a computational approach to data processing that learns the features of its application domain from available representative examples. Being an ANN itself Venn-nets are no exceptions. In order to carry out trainings such as the ones presented on some simulations of this thesis a comprehensive and suitable data set had also to be used. The often selected option in situations such as this is to use known benchmark data. However, given the peculiarities of the simulations of this work a more specific yet laborious alternative was taken. The decision was towards seeking data more closely connected to the nature and objectives of this investigation.

Obviously the choice for the right data set was a very important decision to make, as it would have direct impact on all simulations to be carried out. Therefore, prior to any quest for data to match the purposes of this work we established a number of pre-conditions. They would help not only to select the appropriate data set but would guarantee that all features required from data by all simulations were not missed out during the decision process. The list of considered pre-conditions is enclosed below:

Non-linearity – data should be highly non-linear and non-monotonic to offer a realistic challenge to the neural model proposed

Connection to human cognitive tasks – data should be linked or show similitude to cognitive tasks so that symbolic manipulations might be tried

Sizeable number of patterns – the number of patterns available should not be small to avoid offering restrictions during trainings of networks

Implicit order – an implicit notion of order among patterns is also desirable

Implicit cardinality with in each pattern – patterns should also offer a non-trivial cardinality (i.e. one); this would allow more complex simulations

Extensibility – new patterns should be effortlessly obtainable for eventual further experiments

This strict criterion for data selection ruled out most of the data sets readily available. However, the requirements above do not represent any sort of problem in music. Pentagrams of musical instruments are precisely the sought extensible set of ordered patterns of largely non-linear motor instructions. Following this line, our data quest was reduced merely to what musical instrument to select. Albeit most instruments could serve our purposes, piano seemed to be the best option because generally it’s fair use of the player’s hands and, discrete strokes of each finger. Hence the one selected.

Original data and numerical encoding of finger movements

As explained before piano music was the selected source of data for training the networks in this work. The issue remaining was which piece of music to use. Because of its character yet simplicity Mozart’s Sonata Facile [Koehler51] was the one chosen.

An arbitrary initial portion of the sonata originated 444 training patterns that were produced by encoding the former into numeric values. The usage of fingers in each one of these patterns can be seen in Figure 1.

Figure 1 – Usage of fingers (%) in each pattern within the considered portion of the music

Each pattern produced contains distinct numeric information about flexion of all ten fingers of the piano player in a given time (regardless of their position on the keyboard). In other words, the encoding mechanism relates keystrokes within timestamp (t) to normalized numerical values. The convention used was 0.0; 0.5; and 1.0 to represent respectively: (a) no finger flexion, (b) the same finger flexed after a brief release of a keyboard key and (c) sustained finger flexion on a key. So far only three discrete values are used; in future experiments normalised values between [0, 1] could be used to encode strength of finger flexion. This would be relevant should “real music” is expected. Figure 2 is an example of how the initial portion of the original data [Koehler51] relates to the produced patterns. Red-ticks, i.e. time t(n) is 1/8 of a beat (according to tempo); F₁ to F₁₀ are flexion values from left to right hand little fingers. The creditds for ‘translating’ music notation into the numeric representations devised by the author are due to Flavio Frölich.

Figure 2 – Example of the encoding process carried out for the three first patterns produced

Produced data after encoding of finger movements

Table 1 and Table 2 contain all 444 patterns produced after the encoding described before.

t	Pattern t	Pattern 111+t
1	1 0 0 0 0 1 0 0 0 0	0 1 0 0 0.5 0 1 0 0 0
2	1 0 0 0 0 1 0 0 0 0	1 0 0 0 1 1 0 0 0 0
3	0 0 0 0 1 1 0 0 0 0	1 0 0 0 1 1 0 0 0 0
4	0 0 0 0 1 1 0 0 0 0	1 0 0 0 1 0 1 0 0 0
5	0 0 1 0 0 1 0 0 0 0	1 0 0 0 1 0 0 1 0 0
6	0 0 1 0 0 1 0 0 0 0	0 0 0 0 0 1 0 0 0 0
7	0 0 0 0 1 1 0 0 0 0	0 0 0 0 0 0 1 0 0 0
8	0 0 0 0 1 1 0 0 0 0	0 0 0 0 0 0 0 1 0 0
9	1 0 0 0 0 0 1 0 0 0	0 0 0 0 0 0 0 0 1 0
10	1 0 0 0 0 0 1 0 0 0	0 0 0 0 0 0 0 0 0 1
11	0 0 0 0 1 0 1 0 0 0	0 0 0 0 0 0 0 0 1 0
12	0 0 0 0 1 0 1 0 0 0	0 0 0 0 0 0 0 1 0 0
13	0 0 1 0 0 0 0 0 0 1	0 0 0 0 0 0 1 0 0 0
14	0 0 1 0 0 0 0 0 0 1	1 0 1 0 0 1 0 0 0 0
15	0 0 0 0 1 0 0 0 0 1	1 0 1 0 0 0 0 0 1 0
16	0 0 0 0 1 0 0 0 0 1	1 0 1 0 0 0 0 1 0 0
17	0 1 0 0 0 1 0 0 0 0	1 0 1 0 0 0 1 0 0 0
18	0 1 0 0 0 1 0 0 0 0	0 0 0 1 1 1 0 0 0 0
19	0 0 0 0 1 1 0 0 0 0	0 0 0 1 1 1 0 0 0 0
20	0 0 0 0 1 1 0 0 0 0	0 0 0 1 1 0 1 0 0 0
21	0 0 1 0 0 1 0 0 0 0	0 0 0 1 1 0 0 1 0 0
21	0 0 1 0 0 1 0 0 0 0	0 0 0 1 1 1 0 0 0 0
23	0 0 0 0 1 0 1 0 0 0	0 0 0 1 1 0 1 0 0 0
24	0 0 0 0 1 0 0 1 0 0	0 0 0 1 1 0 0 1 0 0
25	1 0 0 0 0 1 0 0 0 0	0 0 0 1 1 0 0 0 1 0
26	1 0 0 0 0 1 0 0 0 0	0 0 0 1 1 0 0 0 0 1
27	0 0 0 0 1 1 0 0 0 0	0 0 0 1 1 1 0 0 0 0
28	0 0 0 0 1 1 0 0 0 0	0 0 0 1 1 0 1 0 0 0
29	0 0 1 0 0 0 0 0 0 0	0 0 0 1 1 0 0 1 0 0
30	0 0 1 0 0 0 0 0 0 0	0 0 0 1 1 1 0 0 0 0
31	0 0 0 0 1 0 0 0 0 0	0 0 0 1 1 0 1 0 0 0
32	0 0 0 0 1 0 0 0 0 0	0 0 0 1 1 0 0 1 0 0
33	1 0 0 0 0 0 0 1 0 0	0 0 0 1 1 1 0 0 0 0
34	1 0 0 0 0 0 0 1 0 0	0 0 1 0 0 0 1 0 0 0
35	0 0 0 0 1 0 0 1 0 0	0 0 1 0 0 0 0 1 0 0
36	0 0 0 0 1 0 0 1 0 0	0 0 1 0 0 0 0 0 1 0
37	0 1 0 0 0 0 0 1 0 0	0 0 1 0 0 0 0 1 0 0
38	0 1 0 0 0 0 0 1 0 0	0 0 1 0 0 0 1 0 0 0
39	0 0 0 0 1 0 0 1 0 0	0 0 1 0 0 1 0 0 0 0
40	0 0 0 0 1 0 0 1 0 0	0 0 0 1 0 0 0 1 0 0
41	1 0 0 0 0 0 1 0 0 0	0 0 0 1 0 0 1 0 0 0
42	1 0 0 0 0 0 1 0 0 0	0 0 0 0 1 0 0 1 0 0
43	0 0 0 0 1 0 1 0 0 0	0 0 0 0 1 0 0 0 1 0
44	0 0 0 0 1 0 1 0 0 0	0 0 0 0 1 0 0 0 0 1
45	0 0 1 0 0 0 0 0 0 1	0 0 0 0 1 0 0 0 1 0
46	0 0 1 0 0 0 0 0 0 1	0 0 0 0 1 0 0 1 0 0
47	0 0 0 0 1 0 0 0 0 1	0 0 0 0 1 0 1 0 0 0
48	0 0 0 0 1 0 0 0 0 1	0 0 0 1 0 1 0 0 0 0
49	1 0 0 0 0 0 0 1 0 0	0 0 0 1 0 0 1 0 0 0
50	1 0 0 0 0 0 0 1 0 0	1 0 0 0 0 1 0 0 0 0
51	0 0 0 0 1 0 0 1 0 0	0 1 0 0 0 1 0 0 0 0
52	0 0 0 0 1 0 0 1 0 0	0 0 0 1 0 0 0 0 0 1
53	0 1 0 0 0 0 1 0 0 0	0 0 0 0 1 0 0 0 0 1
54	0 1 0 0 0 0 1 0 0 0	1 0 0 0 0 0 0 1 0 0
55	0 0 0 0 1 1 0 0 0 0	0 0 1 0 0 0 0 1 0 0
56	0 0 0 0 1 0 0 0 1 0	0 0 0 1 0 1 0 0 0 0
57	1 0 0 0 0 0 1 0 0 0	0 0 0 0 1 1 0 0 0 0
58	1 0 0 0 0 0 1 0 0 0	1 0 0 0 0 0 1 0 0 0
59	0 0 0 0 1 0 1 0 0 0	0 1 0 0 0 0 1 0 0 0
60	0 0 0 0 1 0 1 0 0 0	0 0 0 1 0 0 0 0 0 1
61	0 0 1 0 0 0 0 0 0 0	0 0 0 0 1 0 0 0 0 1
62	0 0 1 0 0 0 0 0 0 0	1 0 0 0 0 0 0 1 0 0
63	0 0 0 0 1 0 0 0 0 0	0 0 1 0 0 0 0 1 0 0
64	0 0 0 0 1 0 0 0 0 0	0 0 0 1 0 1 0 0 0 0
65	0 1 0 0 0 1 0 0 0 0	0 0 0 0 1 1 0 0 0 0
66	0 1 0 0 0 1 0 0 0 0	1 0 0 0 0 0 1 0 0 0
67	0 1 0 0 0 0 1 0 0 0	1 0 0 0 0 0 1 0 0 0
68	0 1 0 0 0 0 0 1 0 0	1 0 0 0 0 0 1 0 0 0
69	0 0 0 0 0 1 0 0 0 0	1 0 0 0 0 0 0.5 0 0 0
70	0 0 0 0 0 0 1 0 0 0	0 0 0 0 1 1 1 0 0 1
71	0 0 0 0 0 0 0 1 0 0	0 0 0 0 1 1 1 0 0 1
72	0 0 0 0 0 0 0 0 1 0	0 0 0 0 1 1 1 0 0 1
73	0 0 0 0 0 0 0 0 0 1	0 0 0 0 1 1 0.5 0 0 1
74	0 0 0 0 0 0 0 0 1 0	1 0 0 0 0 0 1 0 0 0
75	0 0 0 0 0 0 0 1 0 0	1 0 0 0 0 0 1 0 0 0
76	0 0 0 0 0 0 1 0 0 0	1 0 0 0 0 0 1 0 0 0
77	0 1 0 0 1 1 0 0 0 0	1 0 0 0 0 0 1 0 0 0
78	0 1 0 0 1 0 0 0 1 0	0 0 0 0 0 0 0 0 0 0
79	0 1 0 0 1 0 0 1 0 0	0 0 0 0 0 0 0 0 0 0
70	0 1 0 0 0.5 0 1 0 0 0	0 0 0 0 0 0 0 0 0 0
81	1 0 0 0 1 1 0 0 0 0	0 0 0 0 0 0 0 0 0 0
82	1 0 0 0 1 1 0 0 0 0	0 0 1 0 0 0 0 0 0 0
83	1 0 0 0 1 0 1 0 0 0	0 0 0 0 1 0 0 0 0 0
84	1 0 0 0 1 0 0 1 0 0	0 0 0 1 0 0 0 0 0 0
85	0 0 0 0 0 1 0 0 0 0	0 0 0 0 1 0 0 0 0 0
86	0 0 0 0 0 0 1 0 0 0	0 0 1 0 0 0 0 0 0 0
87	0 0 0 0 0 0 0 1 0 0	0 0 0 0 1 0 0 0 0 0
88	0 0 0 0 0 0 0 0 1 0	0 0 0 1 0 0 0 0 0 0
89	0 0 0 0 0 0 0 0 0 1	0 0 0 0 1 0 0 0 0 0
90	0 0 0 0 0 0 0 0 1 0	0 0 1 0 0 0 0 0 0 0
91	0 0 0 0 0 0 0 1 0 0	0 0 0 0 1 0 0 0 0 0
92	0 0 0 0 0 0 1 0 0 0	0 0 0 1 0 0 0 0 0 0
93	0 1 0 0 1 1 0 0 0 0	0 0 0 0 1 0 0 0 0 0
94	0 1 0 0 1 0 0 0 1 0	0 0 1 0 0 0 0 0 0 0
95	0 1 0 0 1 0 0 1 0 0	0 0 0 0 1 0 0 0 0 0
96	0 1 0 0 0.5 0 1 0 0 0	0 0 0 1 0 0 0 0 0 0
97	1 0 0 0 1 1 0 0 0 0	0 0 0 0 1 0 0 0 0 0
98	1 0 0 0 1 1 0 0 0 0	0 0 1 0 0 0 0 0 0 1
99	1 0 0 0 1 0 1 0 0 0	0 0 0 0 1 0 0 0 0 1
100	1 0 0 0 1 0 0 1 0 0	0 0 1 0 0 0 0 1 0 0
101	0 0 0 0 0 1 0 0 0 0	0 0 0 0 1 0 0 1 0 0
102	0 0 0 0 0 0 1 0 0 0	0 0 1 0 0 1 0 0 0 0
103	0 0 0 0 0 0 0 1 0 0	0 0 0 0 1 1 0 0 0 0
104	0 0 0 0 0 0 0 0 1 0	0 0 1 0 0 1 0 0 0 0
105	0 0 0 0 0 0 0 0 0 1	0 0 0 0 1 1 0 0 0 0
106	0 0 0 0 0 0 0 0 1 0	0 0 1 0 0 1 0 0 0 0
107	0 0 0 0 0 0 0 1 0 0	0 0 0 0 1 1 0 0 0 0
108	0 0 0 0 0 0 1 0 0 0	0 0 1 0 0 0 1 0 0 0
109	0 1 0 0 1 1 0 0 0 0	0 0 0 0 1 0 0 1 0 0
110	0 1 0 0 1 0 0 0 1 0	0 0 1 0 0 0 1 0 0 0
111	0 1 0 0 1 0 0 1 0 0	0 0 0 0 1 0 1 0 0 0

Table 1 – Initial 222 patterns produced based on Mozart’s Sonata Facile

t	Pattern 222+t	Pattern 333+t
1	0 0 1 0 0 1 0 0 0 0	0 0 0 0 1 0 0 0 0 1
2	0 0 0 0 1 1 0 0 0 0	0 0 0 0 1 0 0 1 0 0
3	0 0 0 1 0 0 0 1 0 0	0 0 0 0 1 0 1 0 0 0
4	0 0 0 0 1 0 0 1 0 0	0 1 0 1 0 1 0 0 0 0
5	0 0 1 0 0 0 0 1 0 0	0 0.5 0 0.5 0 1 0 0 0 0
6	0 0 0 0 1 0 1 0 0 0	0 1 0 1 0 1 0 0 0 0
7	0 1 0 0 0 0 0 1 0 0	0 0.5 0 0.5 0 1 0 0 0 0
8	0 0 0 0 1 0 0 1 0 0	0 1 0 1 0 1 0 0 0 0
9	0 0 1 0 0 0 0 1 0 0	0 0.5 0 0.5 0 1 0 0 0 0
10	0 0 0 0 1 0 0 1 0 0	0 1 0 1 0 1 0 0 0 0
11	0 0 0 1 0 0 0 0 0 0	0 0.5 0 0.5 0 1 0 0 0 0
12	0 0 0 0 1 0 0 0 0 0	0 1 0 1 0 0 0 1 0 0
13	0 0 1 0 0 0 0 0 0 0	0 0.5 0 0.5 0 0 0 1 0 0
14	0 0 0 0 1 0 0 0 0 0	0 1 0 1 0 0 0 1 0 0
15	0 0 0 1 0 0 0 0 0 0	0 0.5 0 0.5 0 0 0 0.5 0 0
16	0 0 0 0 1 0 0 0 0 0	0 1 0 1 0 0 0 1 0 0
17	0 1 0 0 0 0 0 0 0 0	0 0.5 0 0.5 0 0 0 1 0 0
18	0 0 0 0 1 0 0 0 0 0	0 1 0 1 0 0 0 1 0 0
19	0 0 1 0 0 0 0 0 0 1	0 0.5 0 0.5 0 0 0 0.5 0 0
20	0 0 0 0 1 0 0 0 0 1	0 1 0 1 0 0 0 1 0 0
21	0 0 1 0 0 0 0 1 0 0	0 0.5 0 0.5 0 0 0 1 0 0
21	0 0 0 0 1 0 0 1 0 0	0 1 0 1 0 0 0 1 0 0
23	0 0 1 0 0 1 0 0 0 0	0 0.5 0 0.5 0 0 0 1 0 0
24	0 0 0 0 1 1 0 0 0 0	0 1 0 1 0 0 0 1 0 0
25	0 0 1 0 0 1 0 0 0 0	0 0.5 0 0.5 0 0 0 1 0 0
26	0 0 0 0 1 1 0 0 0 0	0 1 0 1 0 0 0 1 0 0
27	0 0 1 0 0 1 0 0 0 0	0 0.5 0 0.5 0 0 0 1 0 0
28	0 0 0 0 1 1 0 0 0 0	0 1 0 1 0 0 0 0 0 1
29	0 0 1 0 0 0 1 0 0 0	0 0.5 0 0.5 0 0 0 0 0 1
30	0 0 0 0 1 0 0 1 0 0	0 1 0 1 0 0 1 0 0 0
31	0 0 1 0 0 0 1 0 0 0	0 0.5 0 0.5 0 0 1 0 0 0
32	0 0 0 0 1 0 1 0 0 0	0 1 0 1 0 0 0 0 1 0
33	0 0 1 0 0 1 0 0 0 0	0 0.5 0 0.5 0 0 0 0 1 0
34	0 0 0 0 1 1 0 0 0 0	0 1 0 1 0 0 1 0 0 0
35	0 0 0 1 0 0 0 1 0 0	0 0.5 0 0.5 0 0 1 0 0 0
36	0 0 0 0 1 0 0 1 0 0	1 0 0 0 0 0 0 1 0 0
37	0 0 1 0 0 0 0 1 0 0	0 0 0 0 1 0 0 1 0 0
38	0 0 0 0 1 0 1 0 0 0	0 0 0 1 0 1 0 0 0 0
39	0 1 0 0 0 0 0 1 0 0	0 0 0 0 1 1 0 0 0 0
40	0 0 0 0 1 0 0 1 0 0	1 0 0 0 0 0 0 0 0 1
41	0 0 1 0 0 0 0 1 0 0	0 0 0 0 1 0 0 0 0 1
42	0 0 0 0 1 0 0 1 0 0	0 0 0 1 0 0 0 0 0 1
43	0 0 0 1 0 0 0 0 0 0	0 0 0 0 1 0 0 0 0 1
44	0 0 0 0 1 0 0 0 0 0	1 0 0 0 0 0 0 0 0 1
45	0 0 1 0 0 0 0 0 0 0	0 0 0 0 1 0 0 0 0 1
46	0 0 0 0 1 0 0 0 0 0	0 0 0 1 0 0 0 0 0 1
47	0 0 0 1 0 0 0 0 0 0	0 0 0 0 1 0 0 0 0 1
48	0 0 0 0 1 0 0 0 0 0	1 0 0 0 0 0 0 0 1 0
49	0 1 0 0 0 0 0 0 0 0	0 0 0 0 1 0 0 1 0 0
50	0 0 0 0 1 0 0 0 0 0	0 0 0 1 0 0 1 0 0 0
51	0 0 0 0 0 0 0 0 0 1	0 0 0 0 1 1 0 0 0 0
52	1 0 0 0 0 0 0 0 0 1	1 0 0 0 0 0 0 1 0 0
53	0 1 0 0 0 0 0 0 0 1	0 0 0 0 1 0 0 1 0 0
54	0 0 0 1 0 0 0 0 0 1	0 0 1 0 0 0 0 1 0 0
55	0 0 0 0 1 0 0 0 0 0	0 0 0 0 1 0 0 1 0 0
56	0 0 0 0 1 0 0 0 0 1	1 0 0 0 0 0 0 1 0 0
57	0 0 0 0 1 0 0 0 1 0	0 0 0 0 1 0 0 1 0 0
58	0 0 0 0 1 0 1 0 0 0	0 0 1 0 0 0 0 1 0 0
59	0 0 0 0 0 1 0 0 0 0	0 0 0 0 1 0 0 1 0 0
60	1 0 0 0 0 1 0 0 0 0	1 0 0 0 0 0 0 1 0 0
61	0 1 0 0 0 1 0 0 0 0	0 0 0 0 1 0 0 1 0 0
62	0 0 0 1 0 1 0 0 0 0	0 0 1 0 0 0 0 1 0 0
63	0 0 0 0 1 0 0 0 0 0	0 0 0 0 1 0 0 1 0 0
64	0 0 0 0 1 1 0 0 0 0	1 0 0 0 0 0 0 1 0 0
65	0 0 0 0 1 0 1 0 0 0	0 0 0 0 1 0 0 1 0 0
66	0 0 0 0 1 1 0 0 0 0	0 0 1 0 0 0 0 1 0 0
67	0 0 0 0 0 0 0 0 0 1	0 0 0 0 1 0 0 1 0 0
68	1 0 0 0 0 0 0 0 0 1	0 0 1 1 0 0 1 0 0 0
69	0 1 0 0 0 0 0 0 0 1	0 0 1 1 0 0 1 0 0 0
70	0 0 0 1 0 0 0 0 0 1	0 0 1 1 0 0 1 0 0 0
71	0 0 0 0 1 0 0 0 0 0	0 0 1 1 0 0 0.5 0 0 0
72	0 0 0 0 1 0 0 0 0 1	0 0 0 0 0 0 1 0 0 0
73	0 0 0 0 1 0 0 0 1 0	0 0 0 0 0 1 0 0 0 0
74	0 0 0 0 1 0 1 0 0 0	0 0 0 0 0 0 1 0 0 0
75	0 0 0 0 0 1 0 0 0 0	0 0 0 0 0 0 0 0 1 0
76	1 0 0 0 0 1 0 0 0 0	0 0 0 0 0 0 0 0 0 1
77	0 1 0 0 0 1 0 0 0 0	0 0 0 0 0 0 0 0 1 0
78	0 0 0 1 0 1 0 0 0 0	0 0 0 0 0 0 1 0 0 0
79	0 0 0 0 1 0 0 0 0 0	0 0 0 0 0 0 0 0 1 0
70	0 0 0 0 1 1 0 0 0 0	1 0 0 1 1 0 0 0 0 1
81	0 0 0 0 1 0 1 0 0 0	1 0 0 1 1 0 0 1 0 0
82	0 0 0 0 1 1 0 0 0 0	1 0 0 1 1 0 1 0 0 0
83	0 0 0 0 0 0 0 0 0 1	1 0 0 1 0.5 0 0 0 1 0
84	1 0 0 0 0 0 0 0 0 1	0 0 1 0 1 0 0 1 0 0
85	0 1 0 0 0 0 0 0 0 1	0 0 1 0 1 0 0 1 0 0
86	0 0 0 1 0 0 0 0 0 1	0 0 1 0 1 0 0 1 0 0
87	0 0 0 0 1 0 0 0 0 0	0 0 1 0 1 0 0 1 0 0
88	0 0 0 0 1 0 0 0 0 1	0 0 0 0 0 0 1 0 0 0
89	0 0 0 0 1 0 0 0 1 0	0 0 0 0 0 1 0 0 0 0
90	0 0 0 0 1 0 1 0 0 0	0 0 0 0 0 0 1 0 0 0
91	0 0 0 0 0 1 0 0 0 0	0 0 0 0 0 0 0 0 1 0
92	1 0 0 0 0 1 0 0 0 0	0 0 0 0 0 0 0 0 0 1
93	0 1 0 0 0 1 0 0 0 0	0 0 0 0 0 0 0 0 1 0
94	0 0 0 1 0 1 0 0 0 0	0 0 0 0 0 0 1 0 0 0
95	0 0 0 0 1 0 0 0 0 0	0 0 0 0 0 0 0 0 1 0
96	0 0 0 0 1 1 0 0 0 0	1 0 0 1 1 0 0 0 0 1
97	0 0 0 0 1 0 1 0 0 0	1 0 0 1 1 0 0 1 0 0
98	0 0 0 0 1 1 0 0 0 0	1 0 0 1 1 0 1 0 0 0
99	0 0 0 0 0 0 0 0 0 1	1 0 0 1 0.5 0 0 0 1 0
100	1 0 0 0 0 0 0 0 0 1	0 0 1 0 1 0 0 1 0 0
101	0 1 0 0 0 0 0 0 0 1	0 0 1 0 1 0 0 1 0 0
102	0 0 0 1 0 0 0 0 0 1	0 0 1 0 1 0 0 1 0 0
103	0 0 0 0 1 0 0 0 0 0	0 0 1 0 0.5 0 0 1 0 0
104	0 0 0 0 1 0 0 0 0 1	1 0 0 0 1 0 1 0 0 1
105	0 0 0 0 1 0 0 0 1 0	1 0 0 0 1 0 1 0 0 1
106	0 0 0 0 1 0 1 0 0 0	1 0 0 0 1 0 1 0 0 1
107	0 0 0 0 0 1 0 0 0 0	0.5 0 0 0 0.5 0 1 0 0 1
108	1 0 0 0 0 1 0 0 0 0	1 0 0 0 1 1 0 0 1 0
109	0 1 0 0 0 1 0 0 0 0	1 0 0 0 1 1 0 0 1 0
110	0 0 0 1 0 1 0 0 0 0	1 0 0 0 1 1 0 0 1 0
111	0 0 0 0 1 0 0 0 0 0	0.5 0 0 0 1 1 0 0 1 0

Table 2 – Final 222 patterns produced based on Mozart’s Sonata Facile