amueller

##########################################################################
# Maximum Response filterbank from
# http://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html
# based on several edge and bar filters.
# Adapted to Python by Andreas Mueller amueller@ais.uni-bonn.de
# Share and enjoy
#

import numpy as np
import matplotlib.pyplot as plt

flyyufelix

ResNet-101 in Keras

This is an Keras implementation of ResNet-101 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.

ResNet Paper:

Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385

kernel1994

import numpy as np


def largest_indices(array: np.ndarray, n: int) -> tuple:
    """Returns the n largest indices from a numpy array.

    Arguments:
        array {np.ndarray} -- data array
        n {int} -- number of elements to select

dusty-nv

#!/bin/bash
#
# EDIT:  this script is outdated, please see https://forums.developer.nvidia.com/t/pytorch-for-jetson-nano-version-1-6-0-now-available
#
sudo apt-get install python-pip

# upgrade pip
pip install -U pip
pip --version
# pip 9.0.1 from /home/ubuntu/.local/lib/python2.7/site-packages (python 2.7)

aditya00kumar

from sklearn.metrics.pairwise import cosine_similarity

def maximal_marginal_relevance(sentence_vector, phrases, embedding_matrix, lambda_constant=0.5, threshold_terms=10):
    """
    Return ranked phrases using MMR. Cosine similarity is used as similarity measure.
    :param sentence_vector: Query vector
    :param phrases: list of candidate phrases
    :param embedding_matrix: matrix having index as phrases and values as vector
    :param lambda_constant: 0.5 to balance diversity and accuracy. if lambda_constant is high, then higher accuracy. If lambda_constant is low then high diversity.
    :param threshold_terms: number of terms to include in result set

mikeivanov

import math
import primes

def invmod(a, p):
    '''
    http://code.activestate.com/recipes/576737-inverse-modulo-p/
    The multiplicitive inverse of a in the integers modulo p.
    Return b s.t.
    a * b == 1 mod p
    '''

angeris

Initial positions

Player A:
[0-5] [1-3] [1-4] [2-2] [3-4] [4-4] [5-6]
Player B:
[0-0] [0-1] [0-6] [1-1] [1-6] [2-3] [2-6]
Player C:
[0-2] [2-4] [2-5] [3-3] [3-6] [4-5] [4-6]
Player D:
[0-3] [0-4] [1-2] [1-5] [3-5] [5-5] [6-6]

ericshape

import numpy as np
import scipy as sp
import pandas as pd
import sklearn
from matplotlib import pyplot as plt
from sklearn import preprocessing
from sklearn.cross_validation import cross_val_predict
from sklearn import metrics
from sklearn.metrics import classification_report
from itertools import cycle

vedant

#!/usr/bin/env python
# -*- coding: ascii -*-
#-----------------------------------------------------------------------------
"""
Time Series Analysis
pytsa (read "pizza") depends on scipy and numpy.

Pytsa is a simple timeseries utility for python.
It is good for pedagogical purposes, such as to understand moving averages,
linear regression, interpolation, and single/double/triple exponential smoothing.