def main(): parser = argparse.ArgumentParser( description='compares two point cloud models by Chamfer distance') parser.add_argument('target_model_path', type=str, help='path to point cloud file of target model') parser.add_argument('check_model_path', type=str, help='path to point cloud file of check model') parser.add_argument( '-s', '--subset', type=int, help='use only subset of points of target model for distance estimation' ) args = parser.parse_args() target_model = read_model(args.target_model_path) check_model = read_model(args.check_model_path) subset_size = 500 if args.subset: subset_size = args.subset print('subset size: %s' % subset_size) else: print('default subset size: %s' % subset_size) compare(target_model, check_model, subset_size)
def solve_graph(graph, weights, complexity_width, timeout: int = 10, debug=False): cnfpath = "temp-cw.cnf" with open(cnfpath, 'w') as cnffile: enc = SvEncodingWithComplexity(cnffile, graph, weights, debug) enc.encode_sat(complexity_width) print(f"enc: {enc.__class__.__name__}, ({enc.num_clauses} clauses)") if debug: print("encoding done") base_cmd = ["glucose", "-verb=0", "-model"] cmd = base_cmd + [cnfpath, f"-cpu-lim={timeout}"] start = now() proc = subprocess.run(cmd, universal_newlines=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) runtime = now() - start output = proc.stdout model = read_model(output) dec = TwbnDecoder(enc, -1, model, "") elim_order = dec.get_elim_order() # if USE_DD: # enc.debug_dd_counter(model, elim_order) # else: # enc.debug_counters(model, elim_order) tri = dec.get_triangulated().to_undirected() td = TreeDecomposition(tri, elim_order, width=-1) if DRAWING: pos = graphviz_layout(dec.get_triangulated(), prog='dot') nx.draw(dec.get_triangulated(), pos, with_labels=True) plt.show() td.draw() return td
def __init__(self, data_file, features_file, sep, model_file, plot, epochs, lr): self.model_file = model_file self.plot = plot self.epochs = epochs self.model = {} self.ranges = {} self.lr = lr self.acc = [] self.loss = [] self.val_acc = [] self.val_loss = [] # Read data self.data, self.labels = read_data(data_file, sep) if len(self.data) == 0: print("Error : no valid data found in %s" % data_file) exit(0) self.features = self.get_features(features_file) self.classes_column = "Hogwarts House" self.classes = classes_list(self.data, self.classes_column) # Read model if len(model_file): self.model, _, _ = read_model(model_file, self.classes) self.X_train = [] self.X_val = [] self.Y_train = [] self.Y_val = [] self.thetas = [] self.curr_class = ""
def main(): parser = argparse.ArgumentParser(description='Chamfer distance-based KNN') parser.add_argument('target_model_path', type=str, help='path to point cloud file of target model') parser.add_argument('models_dir_path', type=str, help='path to the check models directory') parser.add_argument( '-s', '--subset', type=int, help='use only subset of points of target model for KNN estimation') args = parser.parse_args() target_model = read_model(args.target_model_path) models = read_models(args.models_dir_path) subset_size = 100 if args.subset: subset_size = args.subset print('subset size: %s' % subset_size) else: print('default subset size: %s' % subset_size) dists = find_nearest(target_model, models, subset_size) print('K Nearest Models to %s:' % args.target_model_path) for dist in dists: print('%s:\t%s' % dist)
def solve_bn(data: BNData, treewidth: int, input_file: str, forced_arcs=None, forced_cliques=None, pset_acyc=None, timeout: int = TIMEOUT, domain_sizes=None, debug=False): cnfpath = "temp.cnf" with open(cnfpath, 'w') as cnffile: if domain_sizes is None: enc = TwbnEncoding(data, cnffile, forced_arcs, forced_cliques, pset_acyc, debug) else: enc = CwbnEncoding(data, cnffile, forced_arcs, forced_cliques, pset_acyc, debug) enc.use_dd = True enc.set_weights(weights_from_domain_sizes(domain_sizes)) enc.encode_sat(treewidth) if debug: print("encoding done") if debug: print(f"maxsat stats: {len(enc.vars)} vars, {enc.num_clauses} clauses") #sys.exit() base_cmd = [os.path.join(SOLVER_DIR, "uwrmaxsat"), "-m", "-v0"] cmd = base_cmd + [cnfpath, f"-cpu-lim={timeout}"] start = now() try: output = subprocess.check_output(cmd, universal_newlines=True, stderr=subprocess.STDOUT) except subprocess.CalledProcessError as err: # copy over problematic cnf file errcnf = "error.cnf" shutil.copy(cnfpath, errcnf) if err.stdout is not None: errfilename = "uwrmaxsat-err.log" with open(errfilename, 'w') as errfile: errfile.write(err.stdout) #raise RuntimeError(f"error while running uwrmaxsat on {errcnf}" # f"\nrc: {err.returncode}, check {errfilename}") print(f"error while running uwrmaxsat on {errcnf}" f"\nrc: {err.returncode}, check {errfilename}") raise NoSolutionException("nonzero returncode") else: #raise RuntimeError(f"error while running uwrmaxsat on {errcnf}" # f"\nrc: {err.returncode}, no stdout captured") print(f"error while running uwrmaxsat on {errcnf}" f"\nrc: {err.returncode}, no stdout captured") raise NoSolutionException("nonzero returncode") else: # if no error while maxsat solving runtime = now() - start model = read_model(output) dec = TwbnDecoder(enc, treewidth, model, input_file) return dec.get_bn()
def predict_tm_score(feature_row): global MODEL if MODEL is None: MODEL = utils.read_model() # Start with the intercept tm_score = MODEL.get('intercept', 0.0) # Get rid of the tm-score, if it's in the dictionary actual_tm_score = feature_row.pop('tm-score', None) # Compute the sum of weight vector values and feature values for seq_num in feature_row.keys(): for feat_type in feature_row[seq_num].keys(): for feat_name in feature_row[seq_num][feat_type].keys(): tm_score += feature_row[seq_num][feat_type][feat_name] * \ MODEL[seq_num][feat_type][feat_name] return tm_score
def main(): try: model = utils.read_model() data = pd.read_csv(p +"/data/weather_data.csv") except: print("Problem in loading") return -1 last_row = data[-1:] if not utils.is_last_data_is_new(last_row): print("last data not new") return -1 prevision = model.make_prevision(last_row) prevision.display() print("done!") return 0
def predict(args): """ This is the demonstration for the batch prediction :param args: parameter for model related config """ addr, port, rank, world_size = extract_xgbooost_cluster_env() dmatrix, y_test = read_predict_data(rank, world_size, None) model_path = args.model_path booster = read_model("oss", model_path, args) preds = booster.predict(dmatrix) best_preds = np.asarray([np.argmax(line) for line in preds]) score = precision_score(y_test, best_preds, average='macro') logging.info("Predict accuracy: %f", score)
def main(): logger.info("Make prevision started") try: model = utils.read_model() data = utils.read_data() except: logger.critical("Problem in loading data") return -1 last_row = data[-1:] if not utils.is_last_data_is_new(last_row): logger.warning("last data not new") return -1 prevision = model.make_prevision(last_row) prevision.display() logger.info("Make prevision ended withou errors") return 0
def __init__(self, data_file, sep, model_file): self.model_file = model_file self.model = {} self.ranges = {} self.features = [] self.classes = [] # Read data self.data, _ = read_data(data_file, sep) if len(self.data) == 0: print("Error : no valid data found in %s" % data_file) exit(0) # Read model if len(model_file): self.model, self.ranges, self.classes = read_model( model_file, self.classes) if len(self.model) == 0: print("Error : no model found in %s" % model_file) exit(0) for feat in self.ranges: self.features.append(feat)
import argparse import open3d as o3d import numpy as np from model import Model from utils import read_model, get_subset_model, view_model parser = argparse.ArgumentParser() parser.add_argument('path', type=str, help='path to point cloud file') parser.add_argument('-s', '--subset', type=int, help='use only subset of points') args = parser.parse_args() model = read_model(args.path) print(model.pcd) if args.subset: model = get_subset_model(model, args.subset) view_model(model)
from keras.datasets import cifar10 from keras.utils import np_utils from utils import visualize_dense_activations, visualize_activations, read_model, get_class_data from keras.utils import plot_model from vis.utils import utils import numpy as np from keras import backend as K from matplotlib import pyplot as plt K.set_image_dim_ordering('th') # 1. read the model & pretrained weights model = read_model("saved/final_model.txt", "saved/final_weights.txt") # 2. print summary and plot model architecture print(model.summary()) plot_model(model, to_file='model.png') # 3. get some data to visualize through activations & normalize (X_train, y_train), (X_test, y_test_orig) = cifar10.load_data() X_test = X_test.astype('float32') X_test = X_test / 255.0 y_test = np_utils.to_categorical(y_test_orig) # 3. visualize conv_layer_idx = utils.find_layer_idx(model, "conv2d_1") conv_layer_idx2 = utils.find_layer_idx(model, "conv2d_2") final_layer_idx = utils.find_layer_idx(model, "dense_2") airplanes = get_class_data(X_test, y_test_orig, 0) ships = get_class_data(X_test, y_test_orig, 8)
def get(self, network, model): return utils.read_model(data_path, network, model)
def test_download_model(model_path, args): return read_model(type="local", model_path=model_path, args=args)
import sys sys.path.insert(0, '../') from utils import preprocess, read_model, read_data, create_inverted_index, search df_profiles = None word2vec_profiles = None inverted_index_profiles = None @app.route('/ping') def ping(): return 'pong!' @app.route('/preprocess') def preprocess_string(): words = 'Programador Python avanzando reconocimiento de KPIs y certificación ORACLE' return preprocess(words) @app.route('/query') def query(): query = 'Programador Python avanzando reconocimiento de KPIs y certificación ORACLE' return str(search(query, word2vec_profiles, inverted_index_profiles)) import os if __name__ == '__main__': root_dir = '../../_data/s3/' df_profiles = read_data(root_dir + 'df_profiles_25k.csv') word2vec_profiles = read_model(root_dir + 'word2vec_profiles_25k') inverted_index_profiles = create_inverted_index(df_profiles, word2vec_profiles) app.run()
def get(self, network, model): return utils.read_model(data_path, network, model)
from keras.datasets import cifar10 from keras.utils import np_utils from utils import visualize_dense_activations, visualize_activations, read_model, get_class_data, save_model from keras.utils import plot_model from keras.optimizers import SGD from vis.utils import utils import numpy as np from keras import backend as K from matplotlib import pyplot as plt K.set_image_dim_ordering('th') # 1. read the model & pretrained weights model = read_model("model.txt", "weights.txt") # 2. print summary and plot model architecture print(model.summary()) # 3. get some data (X_train, y_train), (X_test, y_test) = cifar10.load_data() augment = False X_train = X_train.astype('float32') X_test = X_test.astype('float32') if augment: datagen = ImageDataGenerator(featurewise_center=False, featurewise_std_normalization=False, samplewise_center=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=3, shear_range=0.1,