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ASME's Mechanical Engine…ing Toolkit 1997 December
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ASME's Mechanical Engineering Toolkit 1997 December.iso
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atre27.exe
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ATREE_27
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EXAMPLE1.LF
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1992-08-01
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#
#
#---- example1.lf
#
#
#---- This is an lf example that learns both the XOR, NXOR,
#---- and AND functions of two binary inputs.
#
#---- The trees are saved into the file example1.tre
#---- The encodings are saved into the file example1.cod
#---- Note that a saved set of trees must be accompanied
#---- by its corresponding encodings if the tree is to function
#---- properly in future trials where the trees are loaded
#---- instead of generated.
#
#---- Specify tree statements.
tree
#---- Train on trees of 512 leaves.
size = 512
#---- Train until we get 4 elements of the training set right
min correct = 4
#---- or until 10 epochs have passed.
max epochs = 10
#---- Output folded trees for later retrieval and evaluation
save folded tree to "example1.tre"
#---- Specify function statements.
function
#---- Domain dimension MUST be the first statement, followed
#---- by the codomain dimension statement.
domain dimension = 2
#---- We are training on 3 functions at once, XOR, NXOR, and AND
#---- which means there are 3 codimensions.
codomain dimension = 3
#---- Coding output will be saved for use with the trees we are saving.
save coding to "example1.cod"
#---- All dimensions and codimensions are boolean, so specify
#---- bits:stepsize for the encoding of input and output.
coding = 1:1 1:1 1:1 1:1 1:1
#---- Boolean values have 2 quantization levels.
quantization = 2 2 2 2 2
#---- Optional specifications of the largest values in the 5 encodings;
#---- if not specified, then the largest value in the training and test set
#---- is used.
largest = 1 1 1 1 1
#---- Optional specifications of the smallest values in the 5 encodings;
#---- if not specified, then the smallest value in the training and test set
#---- is used.
#---- Note that the smallest values may not equal the largest values.
smallest = 0 0 0 0 0
#---- There are four rows in our training set.
training set size = 4
training set =
# A B A xor B A nxor B A and B
1 1 0 1 1
1 0 1 0 0
0 1 1 0 0
0 0 0 1 0
#---- We will test on the following 4 vectors.
test set size = 4
test set =
# A B A xor B A nxor B A and B
1 1 0 1 1
1 0 1 0 0
0 1 1 0 0
0 0 0 1 0
#---- The following output file should be generated:
#---- The first line indicates how many codomains there are.
#---- The next four lines represent each of the four lines in the test set.
#---- Each value is followed by its corresponding quantization number
#---- in the prescribed encoding scheme. Each codomain is followed
#---- by the corresponding result from the ALN's, along with its quantization
#---- number. Remember, it's not the calculated value that is as important
#---- as the calculated quantization level. You can get more accurate values
#---- by tightening up the encoding: increasing the number of quantization
#---- levels.
#---- After the results is the error histogram, which counts,
#---- for each of the codomains, the number of times the result quantization
#---- level differed from the actual quantization level by n. In this example,
#---- the ALN's executed the test set perfectly, so there are 4 counts for
#---- errors of n = 0 in each of the 3 codomains.
# A B A xor B A xor B result A nxor B A nxor B result A and B A and B result
#3
#1.000000 1 1.000000 1 0.000000 0 0.000000 0 1.000000 1 1.000000 1 1.000000 1 1.000000 1
#1.000000 1 0.000000 0 1.000000 1 1.000000 1 0.000000 0 0.000000 0 0.000000 0 0.000000 0
#0.000000 0 1.000000 1 1.000000 1 1.000000 1 0.000000 0 0.000000 0 0.000000 0 0.000000 0
#0.000000 0 0.000000 0 0.000000 0 0.000000 0 1.000000 1 1.000000 1 0.000000 0 0.000000 0
#
#ERROR HISTOGRAM
#0 errors 4 4 4
#1 errors 0 0 0
#2 errors 0 0 0
#3 errors 0 0 0
#4 errors 0 0 0
#5 errors 0 0 0
#6 errors 0 0 0
#7 errors 0 0 0
#8 errors 0 0 0
#9+ errors 0 0 0