davegreenwood

from __future__ import print_function
import numpy as np
import cv2
import time

np.set_printoptions(formatter={'float': '{: 0.3f}'.format})


def triangulate_nviews(P, ip):
    """

4xle

#!/usr/bin/env node

'use strict';

// Stream unlimited rows into a Sql Server table.

// WARNING!!! WE DROP and RE-CREATE the table. Then stream the data into it.

// Source stream must be an object stream. Object property names must match
// table column names. Since SQL Server isn't case sensitive, don't think case

kmhofmann

Building TensorFlow from source (TF 2.3.0, Ubuntu 20.04)

Why build from source?

The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.

I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).

So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht

carlsmith

import re

def replace(string, substitutions):

    substrings = sorted(substitutions, key=len, reverse=True)
    regex = re.compile('|'.join(map(re.escape, substrings)))
    return regex.sub(lambda match: substitutions[match.group(0)], string)

smhanov

#!/usr/bin/python3
# By Steve Hanov, 2011. Released to the public domain.
# Please see http://stevehanov.ca/blog/index.php?id=115 for the accompanying article.
#
# Based on Daciuk, Jan, et al. "Incremental construction of minimal acyclic finite-state automata." 
# Computational linguistics 26.1 (2000): 3-16.
#
# Updated 2014 to use DAWG as a mapping; see 
# Kowaltowski, T.; CL. Lucchesi (1993), "Applications of finite automata representing large vocabularies", 
# Software-Practice and Experience 1993