conradlee

# Copyright 2019 Parsely
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

conradlee

# Copyright 2019 Parsely
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

conradlee

conradlee

$ ./fb_edges Caltech36 | comm_algs/louvain_wrapper | ./label_feature_matrix Caltech36 | ./infer 10
0.36

conradlee

from datetime import datetime, timedelta

matlab_datenum = 731965.04835648148
python_datetime = datetime.fromordinal(int(matlab_datenum)) + timedelta(days=matlab_datenum%1) - timedelta(days = 366)

conradlee

import csv
import gzip
from contextlib import closing

def photo_tup_generator(in_filename, bb_left, bb_right, bb_bottom, bb_top):
    """                                                                                                                                                                                     
    Note: rows yielded are in utf8 (have not been decoded to unicode)                                                                                                                                                             
    """
    with closing(gzip.open(in_filename, mode="rb", compresslevel=6)) as f:
        reader = csv.reader(f, escapechar='\\', doublequote=True, quotechar='"', quoting=csv.QUOTE_MINIMAL)

conradlee

# First get distributions of lagged dot products in both directions
c1_leading_c2_dps = get_lag_dot_products(c1, c2, lagged_timesteps)
c2_leading_c1_dps = get_lag_dot_products(c2, c1, lagged_timesteps)

# Check whether distributions are the same using paired t-test
t_score, pval = scipy.stats.ttest_rel(c1_leading_c2_dps, c2_leading_c1_dps)

if pval < PVAL_THRESH:
    # Choose direction with larger mean
    if numpy.mean(c1_leading_c2_dps) > numpy.mean(c2_leading_c1_dps):

conradlee

#!/usr/bin/env python
from nose.tools import *
import networkx as nx
from itertools import combinations

def test_overlaping_K5():
    G = nx.Graph()
    G.add_edges_from(combinations(range(5), 2)) # Add a five clique
    G.add_edges_from(combinations(range(2,7), 2)) # Add another five clique
    c = list(nx.k_clique_communities(G, 4))

conradlee

mysql> show variables like '%character%';
+--------------------------+----------------------------+
| Variable_name            | Value                      |
+--------------------------+----------------------------+
| character_set_client     | utf8                       |
| character_set_connection | utf8                       |
| character_set_database   | utf8                       |
| character_set_filesystem | binary                     |
| character_set_results    | utf8                       |
| character_set_server     | utf8                       |

conradlee

import numpy as np
from collections import defaultdict 

def bin_points(X, bin_size, min_bin_freq):
    bin_sizes = defaultdict(int)
    for point in X:
        binned_point = np.cast[np.int32](point / bin_size)
        bin_sizes[tuple(binned_point)] += 1

    bin_seeds = np.array([point for point, freq in bin_sizes.iteritems() if freq >= min_bin_freq], dtype=np.float32)