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

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

kylemcdonald

weiliu620

def dice_loss(pred, target):
    """This definition generalize to real valued pred and target vector.
This should be differentiable.

    pred: tensor with first dimension as batch
    target: tensor with first dimension as batch

    """

    smooth = 1.

ruanbekker

My Elasticsearch cheatsheet with example usage via rest api (still a work-in-progress)

Shortlinks:

• Cluster Health
  • Index Level
  • Shard Level
• Nodes Overview
• Indices Overview
• Cluster Maintenance

rspeer

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

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# EDIT:  this script is outdated, please see https://forums.developer.nvidia.com/t/pytorch-for-jetson-nano-version-1-6-0-now-available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)

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

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]