def main(): # Parse command line arguments. if len(sys.argv) < 4: usage_and_exit() pass_size = int(sys.argv[1]) reg_type = sys.argv[2] reg_fname = sys.argv[3] # Stuff we need to generate a password. reg = None if reg_type == 'nnet': reg = nnet.load(reg_fname) elif reg_type == 'svm': reg = svm.load(reg_fname) elif reg_type == 'knn': reg = knn.load(reg_fname) else: usage_and_exit() feature_fun = features.transform_all all_chars = ''.join(util.NORMAL_LAYOUT) scorer = passgen.Scorer(reg, feature_fun, util.layout_mapping()) # Generate and print a password. print passgen.generate(scorer, all_chars, pass_size, 4)
import sys from argparse import ArgumentParser from iris import feature from skimage.util import img_as_ubyte from skimage.data import imread from svm import SVM, load from features import FeatureExtractor parser = ArgumentParser() parser.add_argument('path') if __name__ == "__main__": args = parser.parse_args() vector = feature(args.path, FeatureExtractor(set(['daisy', 'hog', 'raw']))) classifier = load('rf') prediction = classifier.predict(vector) print >>sys.stderr, prediction if abs(prediction[2][0] - prediction[2][1]) <= 0.05: print >>sys.stderr, "Doubt" exit(-1) print prediction[1].split('/')[-1]
def main(args): # Loading Dataset and Preprocessing Data X_train, Y_train, X_test, Y_test, train_sample, valid_sample, testing_sample = svm.load( args.window_size) # Build Input Fn train_input_fn = svm.np_input_fn(X_train, Y_train, samples=train_sample, shuffle=True, window_size=args.window_size, batch=args.batch, epoch=args.epoch) # Training Model input_dim = args.window_size * args.window_size * 6 if args.model == "linear": estimator = svm.create_linear_model(args.learning_rate, input_dim, config=config(args)) if args.model == "rffm": estimator = svm.create_rffm_model(args.learning_rate, input_dim, args.dimension, args.stddev, config=config(args)) start = time.time() if args.train: estimator.fit(input_fn=train_input_fn) # Train. train_sec = time.time() - start print('Training Elapsed time: {} seconds'.format(train_sec)) # Evaluating Training Data if not args.evaluate: return start = time.time() train_metrics = svm.evaluate_model(estimator, X_train, Y_train, train_sample, batch=2048, window_size=args.window_size) valid_metrics = svm.evaluate_model(estimator, X_train, Y_train, valid_sample, batch=2048, window_size=args.window_size) testing_metrics = svm.evaluate_model(estimator, X_test, Y_test, testing_sample, batch=2048, window_size=args.window_size) eval_sec = time.time() - start print('Evaluate Elapsed time: {} seconds'.format(eval_sec)) train = datastat.sum_stat(Y_train, train_sample)[0] vaild = datastat.sum_stat(Y_train, valid_sample)[0] test = datastat.sum_stat(Y_test, testing_sample)[0] train_metrics["tn"] = train - (train_metrics["tp"] + train_metrics["fp"] + train_metrics["fn"]) valid_metrics["tn"] = vaild - (valid_metrics["tp"] + valid_metrics["fp"] + valid_metrics["fn"]) testing_metrics["tn"] = test - ( testing_metrics["tp"] + testing_metrics["fp"] + testing_metrics["fn"]) print(train_metrics) print(valid_metrics) print(testing_metrics) global_step = estimator.get_variable_value("global_step") result = "%d %d %d %d %d %d %d %d %d %d %d %d %d %d %d %d %d %d %d %s %f %f %f\n" % ( train_metrics["tp"], train_metrics["fp"], train_metrics["fn"], train_metrics["tn"], valid_metrics["tp"], valid_metrics["fp"], valid_metrics["fn"], valid_metrics["tn"], testing_metrics["tp"], testing_metrics["fp"], testing_metrics["fn"], testing_metrics["tn"], global_step, args.epoch, args.batch, args.window_size, args.learning_rate, args.dimension, args.stddev, args.model, train_sec, eval_sec, train_sec + eval_sec) print(result) f = open(args.output, "a+") f.write(result) f.close()
# coding: utf-8 # In[ ]: import fio import svm import numpy as np import itertools # In[ ]: if __name__ == "__main__": X_train, Y_train, X_test, Y_test, train_sample, valid_sample, testing_sample = svm.load(23) # In[ ]: def count(Y, sample): if isinstance(Y, list): arr = np.array([], dtype=int) for y, s in zip(Y, sample): arr = np.append(arr, count(y, s)) return arr.reshape((-1,3)) if sample is None: return np.array([0,0,0])