def gen_weights_all(ddir):
params = import_params(ddir)
try:
nRows = int(params['nRowsIn'])
nCols = int(params['nColsIn'])
nOutputs = int(params['nOutputs'])
except (KeyError, TypeError) as err:
print('necessary parameter not found: ' + str(err))
sys.exit(-1)
nInputs = nRows * nCols
Wx, Wy = get_weights(ddir, nInputs, nOutputs)
if Wx is None or Wy is None:
print("failed to read weights")
return
# take most recent values, strip time and transpose so we can write column wise (like eigen)
Wx, Wy = Wx[-1,1:,:].T, Wy[-1,1:,:].T
# use final value and reshape to row vector
Wx, Wy = np.squeeze(Wx.reshape(-1,1)), np.squeeze(Wy.reshape(-1,1))
# prepend dummy time stamp
Wx, Wy = np.concatenate(([0.0],Wx)), np.concatenate(([0.0],Wy))
save_weights_all(ddir, [Wx], [Wy])
def heatmap(ddir, ts, subplot, quiet, show_title, cmap=None):
params = import_params(ddir)
try:
nRows = int(params['nRowsIn'])
nCols = int(params['nColsIn'])
except KeyError, err:
print('necessary parameter not found: ' + str(err))
sys.exit(-1)
def weights(ddir, ts, quiet, show_title, cmap):
params = import_params(ddir)
try:
nRows = int(params['nRowsIn'])
nCols = int(params['nColsIn'])
nOutputs = int(params['nOutputs'])
except KeyError, err:
print('necessary parameter not found: ' + str(err))
sys.exit(-1)
def force_fields(ddir, ts, wta, continuous, csteps, subplot, quiet, show_title, cmap):
params = import_params(ddir)
try:
nRows = int(params["nRowsIn"])
nCols = int(params["nColsIn"])
nOutputs = int(params["nOutputs"])
popMinX = float(params["popMinX"])
popMaxX = float(params["popMaxX"])
popMinY = float(params["popMinY"])
popMaxY = float(params["popMaxY"])
except KeyError, err:
print("necessary parameter not found: " + str(err))
sys.exit(-1)
def main():
dataset = {
'north_ireland_2012': 'ED-00001-00000001',
'debian_logo': 'ED-00002-00000008'
}
filename = dataset['north_ireland_2012']
# ------------------- Load extra json files -------------------- #
params = utils.import_params(filename=f'json/{filename}')
candidates = utils.import_candidates(filename=f'json/{filename}')
# -------------------- Load preflib files ---------------------- #
pairs = utils.import_pairwise(filename=f'data/{filename}.pwg',
params=params)
vote_matrix, n_votes = utils.import_strict_order(
filename=f'data/{filename}.soi', params=params)
# ------------------- Plurality rule ---------------------------- #
plurality_rule_winner, scores = libchoice.plurality_rule(
votes=vote_matrix, candidates=candidates, n_votes=n_votes)
utils.print_plurality(candidates, scores=scores)
print(f'The plurality rule winner is {plurality_rule_winner}.')
dual_matrix = libchoice.get_dual_matrix_from_pairwise(
pairs=pairs, candidates=candidates)
winner, scores = libchoice.compute_winner(dual_matrix=dual_matrix,
candidates=candidates)
utils.print_table_one_to_one(dual_matrix, candidates)
utils.print_ranking(candidates=candidates, scores=scores, title='Ranking')
# ----------------- Condorcet winner ---------------------------- #
condorcet_winner = libchoice.compute_condorcet_winner(
dual_matrix=dual_matrix, candidates=candidates)
print(f'The condorcet winner is {condorcet_winner}.')
def show_params(d, params_file, recursive):
if not os.path.isdir(d):
return
if recursive:
for dd in os.listdir(d):
dd = os.path.join(d, dd)
if os.path.isdir(dd):
show_params(dd, params_file, recursive)
params = import_params(d, params_file, verbose=False)
if not params:
return
print("experiment {}".format(d))
linear = False
for l, v in params.items():
if l == 'time':
continue
elif l == 'learningRule':
v = LEARNING_RULES[int(params[l])]
elif l == 'outputFn':
v = OUTPUT_FUNCTIONS[int(params[l])]
if v == 'linear':
linear = True
elif l == 'rewardQty':
v = REWARD_QTY[int(params[l])]
elif l == 'activationFn':
v = ACTIVATION_FUNCTIONS[int(params[l])]
else:
v = params[l]
if l == 'sigmoidBeta' and linear:
continue
print(" {:<25}: {}".format(l, v))
print("")
def main():
dataset = {
'north_ireland_2012': 'ED-00001-00000001',
'debian_logo': 'ED-00002-00000008'
}
filename = dataset['debian_logo']
# ------------------- Load extra json files -------------------- #
params = utils.import_params(
filename=f'json/{filename}'
)
candidates = utils.import_candidates(
filename=f'json/{filename}'
)
# -------------------- Load preflib files ---------------------- #
pairs = utils.import_pairwise(
filename=f'data/{filename}.pwg', params=params
)
vote_matrix, n_votes = utils.import_strict_order(
filename=f'data/{filename}.soi', params=params
)
# ------------------- Rule 1 ------------------------------------ #
dual_results = libchoice.get_dual_matrix_from_pairwise(
pairs=pairs, candidates=candidates
)
winner, scores = libchoice.compute_winner(dual_matrix=dual_results, candidates=candidates)
utils.print_ranking(candidates=candidates, scores=scores, title='Rule 1 ranking')
# ------------------- Rule 2 -------------------------------- #
# From pairwise file
paths = schulze.compute_paths(
pairs=pairs, candidates=candidates
)
dual_results_schulze = libchoice.get_dual_matrix_from_pairwise(
pairs=paths, candidates=candidates
)
winner, scores = libchoice.compute_winner(
dual_matrix=dual_results_schulze, candidates=candidates
)
utils.print_table_one_to_one(dual_results_schulze, candidates)
utils.print_ranking(candidates=candidates, scores=scores, title='(Rule 2) Schulz ranking')
# ----------------- Condorcet winner ---------------------------- #
condorcet_winner = libchoice.compute_condorcet_winner(
dual_matrix=dual_results_schulze, candidates=candidates
)
print(f'The condorcet winner is {condorcet_winner}.')
