def run_test(file_path): test_data = loader.load_file(file_path) logger.log_debug(test_data) req_kwargs = test_data['request'] url = req_kwargs.pop('url') method = req_kwargs.pop('method') print(test_data) resp_obj = requests.request(url=url, method=method, **req_kwargs) return resp_obj
def start_emulator(): pygame.init() initialize() # Start memory mem.initialize() # Load rom loader.load_file('SuperMarioBros(E).nes') # Start CPU cpu.initialize() # Debug functions # Emulation Loop while True: cpu.cycle() ppu.cycle()
def process_file(file): """ Given a file, load it, parse it, convert all headlines to POS tries, and save the graph """ logging.info("Loading File: {}".format(file)) data = load_file(file) g = nx.DiGraph() g.add_node('__ROOT') for doc in data: if 'headline' not in doc or 'main' not in doc['headline']: IPython.embed(simple_prompt=True) continue logging.info("Processing: {}".format(doc['headline']['main'])) path = "__ROOT" p_text = nlp(doc['headline']['main']) IPython.embed(simple_prompt=True) for tok in p_text: tag = tok.tag_ new_path = path + "." + tag if not g.has_edge(path, new_path): g.add_node(new_path, tag=tag, word_set=set()) g.add_edge(path,new_path, weight=0) g[path][new_path]['weight'] += 1 g.nodes[new_path]['word_set'].add(tok.text) path = new_path if 'keywords' not in g.nodes[path]: g.nodes[path]['keywords'] = [] g.nodes[path]['keywords'] += doc['keywords'] if '_ids' not in g.nodes[path]: g.nodes[path]['_ids'] = [] g.nodes[path]['_id'] += doc['_id'] g.nodes[path]['LEAF'] = True # All docs processed, save nx.write_gpickle(g, join(grammar_dir,file + ".pkl"))
def main(path): with open(path, 'rt') as f: keyfun = lambda item: (item.size, item.distribution_name) data = groupby(load_file(f), keyfun) header1 = [("", 3), ("speedup over %s procedure" % reference_procedure, len(procedures) * 3)] header2 = [("", 3)] header3 = ["size [B]", "distribution", "samples"] for proc in procedures: header2.append((proc, 3)) header3.extend(["min", "avg", "max"]) table = Table() table.add_header(header1) table.add_header(header2) table.add_header(header3) for key in sorted(data): collection = data[key] size, name, stats = calculate_speedup_statistics(collection) row = [] row.append('%d' % size) row.append(get_distribution_title(name)) row.append('%d' % len(collection)) for proc in procedures: row.append('%0.2f' % stats[proc][0]) row.append('%0.2f' % stats[proc][1]) row.append('%0.2f' % stats[proc][2]) table.add_row(row) print table
def get_data_series(self): persistant_storage = PersistentStorage() try: last_path_used = persistant_storage.get_value("fromfile_last_dir_used") except KeyError: last_path_used = "" #get filename to open file_to_open = wx.FileSelector("Choose file to open", default_path=last_path_used) if file_to_open == "": return persistant_storage.set_value("fromfile_last_dir_used", os.path.dirname(file_to_open)) wx.BeginBusyCursor() try: contents = loader.load_file(file_to_open) series_select_dialog = TxtFileDataSeriesSelectFrame(self.get_parent(), contents) finally: wx.EndBusyCursor() if series_select_dialog.ShowModal() == wx.ID_OK: return series_select_dialog.get_series()
if __name__ == '__main__': parser = a.ArgumentParser(description='A simple fof solver using tptp syntax') parser.add_argument('--file', action='store') parser.add_argument('--jsonfile', action='store') parser.add_argument('--formula', action='store') args = vars(parser.parse_args()) if args['file']: fof_data = l.parse_and_load(args['file']) #fof_data = from_file(args['file']) elif args['formula']: fof_data = from_string("fof(ax,axiom," + args['formula'] + ").") elif args['jsonfile']: fof_data = l.load_file(args['jsonfile']) else : string = "fof(ax, axiom, ![X]: r(X) => ?[Y]:r(Y) ).fof(ax, conjecture, ![X]: r(X) => ?[Y]:r(Y) )." fof_data = from_string(string) print("input formula:",fof_data) conjectures = [] axioms = [] for formula in fof_data: if formula['type'] in ('axiom', 'theorem'): formula = o.transform(formula['formula']) axioms.append(formula) elif formula['type'] in ('conjecture'): f = formula['formula'].negate() f = o.transform(f) conjectures.append(f)
try: xrange except: xrange = range def totalvalue(comb): " Totalise a particular combination of items" totwt = totval = 0 for item, wt, val in comb: totwt += wt totval += val return (totval, -totwt) if totwt <= 400 else (0, 0) capacity, items = load_file("input") def knapsack01_dp(items, limit): table = [[0 for w in range(limit + 1)] for j in xrange(len(items) + 1)] for j in xrange(1, len(items) + 1): item, wt, val = items[j - 1] for w in xrange(1, limit + 1): if wt > w: table[j][w] = table[j - 1][w] else: table[j][w] = max(table[j - 1][w], table[j - 1][w - wt] + val) result = [] w = limit
def assets(path): ext = request.path.split("/")[-1].split(".")[-1] return Response(load_file(request.path), mimetype=MIME_TYPE[ext])
import sys sys.path.append('/home/barbara/scheduling_procedures/TAEMS') sys.path.append('/home/barbara/scheduling_procedures/Genetic algorithm') sys.path.append('/home/barbara/scheduling_procedures/Tabu search') sys.path.append('/home/barbara/scheduling_procedures/Simulated annealing') sys.path.append('/home/barbara/scheduling_procedures/Ant colony optimization') from taems import TaemsTree import genetic_algorithm import tabu_search import simulated_annealing import ant_colony_optimization if __name__ == "__main__": [alternative, pt, rt, dt] = loader.load_file("./Input/", "alternative_1.txt") tree = TaemsTree.load_from_file("./Input/example_large.taems") test = 2 if test == 0: ga_solutions = [] ga = genetic_algorithm.GeneticAlgorithm(200, 0.1, 0.3) for i in range(1000): [best_sol, iteration] = ga.optimize(tree, alternative, pt, rt, dt, 50) print([i, best_sol.total_tardiness, iteration]) ga_solutions.append([best_sol, iteration]) f = open("Results/ga_results.txt", "w") for i in range(1000): f.write(str(ga_solutions[i][0].total_tardiness) + " " + str(ga_solutions[i][1]) + "\n")
import loader import tables_to_leave import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.externals.joblib import load, dump LOAD_PATH = 'C:\\Users\\Tom\\PycharmProjects\\wiseNeuro\\data\\pred.csv' SAVE_PATH = 'C:\\Users\\Tom\\PycharmProjects\\wiseNeuro\\data\\prediction.csv' df = loader.load_file(LOAD_PATH, ',') df = loader.drop_tables(df, tables_to_leave.TABLE_LIST) df = df.fillna(0) df = df.to_numpy(dtype=float) scaler = load('std_scaler.bin') #scaler = StandardScaler() #scaler.fit(df) df = scaler.transform(df) model = loader.load_model() prediction = model.predict(df) newframe = pd.DataFrame() newframe['result'] = prediction.flatten().astype(float) def drop_result(df): df_head = list(df) for head in df_head: if head not in ('RAJ2000', 'DEJ2000'): del df[head] return df
from __future__ import absolute_import, division, print_function, unicode_literals from sklearn.preprocessing import StandardScaler import pandas as pd import tensorflow as tf import loader import tables_to_leave from sklearn.externals.joblib import dump, load KNOWN_PATCH = 'C:\\Users\\Tom\\PycharmProjects\\wiseNeuro\\data\\known.csv' SKY_PATCH = 'C:\\Users\\Tom\\PycharmProjects\\wiseNeuro\\data\\sky_train.csv' TEST_PATH = '' known_df = loader.load_file(KNOWN_PATCH, sep=';') known_df['isdwarf'] = 1 known_df = loader.drop_tables(known_df, tables_to_leave.TABLE_LIST) print(known_df.head()) sky_df = loader.load_file(SKY_PATCH, sep=',') sky_df['isdwarf'] = 0 sky_df = loader.drop_tables(sky_df, tables_to_leave.TABLE_LIST) print(sky_df.head()) df = pd.concat([known_df, sky_df], sort=False) df = df.fillna(0) df_len = len(df.columns) print(df_len) target = df.pop('isdwarf') scaler = StandardScaler() scaler.fit(df) dump(scaler, 'std_scaler.bin', compress=True) df = scaler.transform(df) dataset = tf.data.Dataset.from_tensor_slices((df, target.values)) train_dataset = dataset.shuffle(len(df)).batch(1)
return ( comb for r in range(1, len(items) + 1) for comb in combinations(items, r) ) def totalvalue(comb): ' Totalise a particular combination of items' totwt = totval = 0 for item, wt, val in comb: totwt += wt totval += val return (totval, -totwt) if totwt <= 400 else (0, 0) capacity, items = load_file('input') def fil(x): val, dummy = totalvalue(x) return val def fil2(x): dummy, wt = totalvalue(x) return wt xd = [x for x in anycomb(items) if -fil2(x) <= capacity] bagged = max(xd, key=totalvalue)
if __name__ == '__main__': parser = a.ArgumentParser( description='A simple fof solver using tptp syntax') parser.add_argument('--file', action='store') parser.add_argument('--jsonfile', action='store') parser.add_argument('--formula', action='store') args = vars(parser.parse_args()) if args['file']: fof_data = l.parse_and_load(args['file']) #fof_data = from_file(args['file']) elif args['formula']: fof_data = from_string("fof(ax,axiom," + args['formula'] + ").") elif args['jsonfile']: fof_data = l.load_file(args['jsonfile']) else: string = "fof(ax, axiom, ![X]: r(X) => ?[Y]:r(Y) ).fof(ax, conjecture, ![X]: r(X) => ?[Y]:r(Y) )." fof_data = from_string(string) print("input formula:", fof_data) conjectures = [] axioms = [] for formula in fof_data: if formula['type'] in ('axiom', 'theorem'): formula = o.transform(formula['formula']) axioms.append(formula) elif formula['type'] in ('conjecture'): f = formula['formula'].negate() f = o.transform(f) conjectures.append(f)