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Automatic search for quantization bounds
This uses the expected informational gain to determine where quantization bounds should
lie.
**Notes**:
- bounds are less than, so if the bounds are [1.,2.],
[0.9,1.,1.1,2.,2.2] -> [0,1,1,2,2]
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floating point equality with a tolerance factor **Arguments** - v1: a float - v2: a float - tol: the tolerance for comparison **Returns** 0 or 1 |
Uses FindVarMultQuantBounds, only here for historic reasons |
Primarily intended for internal use
constructs a variable table for the data passed in
The table for a given variable records the number of times each possible value
of that variable appears for each possible result of the function.
**Arguments**
- vals: a 1D Numeric array with the values of the variables
- cuts: a list with the indices of the quantization bounds
(indices are into _starts_ )
- starts: a list of potential starting points for quantization bounds
- results: a 1D Numeric array of integer result codes
- nPossibleRes: an integer with the number of possible result codes
**Returns**
the varTable, a 2D Numeric array which is nVarValues x nPossibleRes
**Notes**
- _vals_ should be sorted!
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Primarily intended for internal use
Recursively finds the best quantization boundaries
**Arguments**
- vals: a 1D Numeric array with the values of the variables,
this should be sorted
- cuts: a list with the indices of the quantization bounds
(indices are into _starts_ )
- which: an integer indicating which bound is being adjusted here
(and index into _cuts_ )
- starts: a list of potential starting points for quantization bounds
- results: a 1D Numeric array of integer result codes
- nPossibleRes: an integer with the number of possible result codes
**Returns**
- a 2-tuple containing:
1) the best information gain found so far
2) a list of the quantization bound indices ( _cuts_ for the best case)
**Notes**
- this is not even remotely efficient, which is why a C replacement
was written
|
Primarily intended for internal use
Recursively finds the best quantization boundaries
**Arguments**
- vals: a 1D Numeric array with the values of the variables,
this should be sorted
- cuts: a list with the indices of the quantization bounds
(indices are into _starts_ )
- which: an integer indicating which bound is being adjusted here
(and index into _cuts_ )
- starts: a list of potential starting points for quantization bounds
- results: a 1D Numeric array of integer result codes
- nPossibleRes: an integer with the number of possible result codes
**Returns**
- a 2-tuple containing:
1) the best information gain found so far
2) a list of the quantization bound indices ( _cuts_ for the best case)
**Notes**
- this is not even remotely efficient, which is why a C replacement
was written
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finds multiple quantization bounds for a single variable
**Arguments**
- vals: sequence of variable values (assumed to be floats)
- nBounds: the number of quantization bounds to find
- results: a list of result codes (should be integers)
- nPossibleRes: an integer with the number of possible values of the
result variable
**Returns**
- a 2-tuple containing:
1) a list of the quantization bounds (floats)
2) the information gain associated with this quantization
|
Primarily intended for internal use
Recursively finds the best quantization boundaries
**Arguments**
- vals: a 1D Numeric array with the values of the variables,
this should be sorted
- cuts: a list with the indices of the quantization bounds
(indices are into _starts_ )
- which: an integer indicating which bound is being adjusted here
(and index into _cuts_ )
- starts: a list of potential starting points for quantization bounds
- results: a 1D Numeric array of integer result codes
- nPossibleRes: an integer with the number of possible result codes
**Returns**
- a 2-tuple containing:
1) the best information gain found so far
2) a list of the quantization bound indices ( _cuts_ for the best case)
**Notes**
- this is not even remotely efficient, which is why a C replacement
was written
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