mirror of https://github.com/openXC7/prjxray.git
135 lines
3.9 KiB
Python
135 lines
3.9 KiB
Python
#!/usr/bin/env python3
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from timfuz import Benchmark, Ar_di2np, Ar_ds2t, A_di2ds, A_ds2di, loadc_Ads_b, index_names, A_ds2np, load_sub, run_sub_json
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import numpy as np
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import glob
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import json
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import math
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from collections import OrderedDict
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from fractions import Fraction
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def Adi2matrix_random(A_ubd, b_ub, names):
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# random assignment
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# was making some empty rows
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A_ret = [np.zeros(len(names)) for _i in range(len(names))]
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b_ret = np.zeros(len(names))
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for row, b in zip(A_ubd, b_ub):
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# Randomly assign to a row
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dst_rowi = random.randint(0, len(names) - 1)
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rownp = Ar_di2np(row, cols=len(names), sf=1)
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A_ret[dst_rowi] = np.add(A_ret[dst_rowi], rownp)
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b_ret[dst_rowi] += b
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return A_ret, b_ret
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def Ads2matrix_linear(Ads, b):
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names, Adi = A_ds2di(Ads)
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cols = len(names)
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rows_out = len(b)
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A_ret = [np.zeros(cols) for _i in range(rows_out)]
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b_ret = np.zeros(rows_out)
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dst_rowi = 0
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for row_di, row_b in zip(Adi, b):
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row_np = Ar_di2np(row_di, cols)
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A_ret[dst_rowi] = np.add(A_ret[dst_rowi], row_np)
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b_ret[dst_rowi] += row_b
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dst_rowi = (dst_rowi + 1) % rows_out
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return A_ret, b_ret
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def pmatrix(Anp, s):
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import sympy
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msym = sympy.Matrix(Anp)
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print(s)
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sympy.pprint(msym)
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def pds(Ads, s):
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names, Anp = A_ds2np(Ads)
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pmatrix(Anp, s)
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print('Names: %s' % (names, ))
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def run(fns_in, sub_json=None, verbose=False):
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assert len(fns_in) > 0
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# arbitrary corner...data is thrown away
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Ads, b = loadc_Ads_b(fns_in, "slow_max")
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if sub_json:
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print('Subbing JSON %u rows' % len(Ads))
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#pds(Ads, 'Orig')
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names_old = index_names(Ads)
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run_sub_json(Ads, sub_json, verbose=verbose)
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names_new = index_names(Ads)
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print("Sub: %u => %u names" % (len(names_old), len(names_new)))
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print(names_new)
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print('Subbed JSON %u rows' % len(Ads))
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names = names_new
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#pds(Ads, 'Sub')
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else:
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names = index_names(Ads)
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# Squash into a matrix
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# A_ub2, b_ub2 = Adi2matrix_random(A_ubd, b, names)
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Amat, _bmat = Ads2matrix_linear(Ads, b)
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#pmatrix(Amat, 'Matrix')
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'''
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The matrix must be fully ranked to even be considered reasonable
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Even then, floating point error *possibly* could make it fully ranked, although probably not since we have whole numbers
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Hence the slogdet check
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'''
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print
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# https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.linalg.matrix_rank.html
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rank = np.linalg.matrix_rank(Amat)
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print('rank: %s / %d col' % (rank, len(names)))
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# doesn't work on non-square matrices
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if 0:
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# https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.linalg.slogdet.html
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sign, logdet = np.linalg.slogdet(Amat)
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# If the determinant is zero, then sign will be 0 and logdet will be -Inf
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if sign == 0 and logdet == float('-inf'):
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print('slogdet :( : 0')
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else:
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print('slogdet :) : %s, %s' % (sign, logdet))
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if rank != len(names):
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raise Exception(
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"Matrix not fully ranked w/ %u / %u" % (rank, len(names)))
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def main():
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import argparse
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parser = argparse.ArgumentParser(
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description='Check if sub.json would make a linear equation solvable')
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parser.add_argument('--verbose', action='store_true', help='')
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parser.add_argument('--sub-json', help='')
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parser.add_argument('fns_in', nargs='*', help='timing4i.csv input files')
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args = parser.parse_args()
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# Store options in dict to ease passing through functions
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bench = Benchmark()
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fns_in = args.fns_in
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if not fns_in:
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fns_in = glob.glob('specimen_*/timing4i.csv')
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sub_json = None
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if args.sub_json:
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sub_json = load_sub(args.sub_json)
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try:
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run(sub_json=sub_json, fns_in=fns_in, verbose=args.verbose)
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finally:
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print('Exiting after %s' % bench)
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if __name__ == '__main__':
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main()
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