willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

suxue

# Coverage targets

if HAVE_GCOV

.PHONY: clean-gcda
clean-gcda:
	@echo Removing old coverage results
	-find -name '*.gcda' -print | xargs -r rm

.PHONY: coverage-html generate-coverage-html clean-coverage-html

suxue

linux.img
.lock
record
.gdbinit

freemo

import com.thinkaurelius.titan.core.TitanFactory;
import com.thinkaurelius.titan.core.TitanGraph;
import com.thinkaurelius.titan.core.TitanKey;
import com.thinkaurelius.titan.core.attribute.Geoshape;
import com.thinkaurelius.titan.graphdb.configuration.GraphDatabaseConfiguration;
import com.tinkerpop.blueprints.Edge;
import com.tinkerpop.blueprints.Vertex;
import com.tinkerpop.blueprints.util.ElementHelper;
import org.apache.commons.configuration.BaseConfiguration;
import org.apache.commons.configuration.Configuration;

hyper0x

package main

import (
	"bufio"
	"bytes"
	"fmt"
	"io"
	"log"
	"net"
	"os"

joar

# Add field
echo '{"hello": "world"}' | jq --arg foo bar '. + {foo: $foo}'
# {
#   "hello": "world",
#   "foo": "bar"
# }

# Override field value
echo '{"hello": "world"}' | jq --arg foo bar '. + {hello: $foo}'
{

EthanRosenthal

from numpy.linalg import solve

class ExplicitMF():
    def __init__(self, 
                 ratings, 
                 n_factors=40, 
                 item_reg=0.0, 
                 user_reg=0.0,
                 verbose=False):
        """

nzw0301

*2vec papers

1. act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
2. apk2vec https://arxiv.org/abs/1809.05693
3. app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
4. ast2vec https://arxiv.org/abs/2103.11614
5. attribute2vec https://arxiv.org/abs/2004.01375
6. author2vec http://dl.acm.org/citation.cfm?id=2889382
7. baller2vec https://arxiv.org/abs/2102.03291
8. bb2vec https://arxiv.org/abs/1809.09621

msmfsd

// Async/Await requirements: Latest Chrome/FF browser or Babel: https://babeljs.io/docs/plugins/transform-async-to-generator/
// Fetch requirements: Latest Chrome/FF browser or Github fetch polyfill: https://github.com/github/fetch

// async function
async function fetchAsync () {
  // await response of fetch call
  let response = await fetch('https://api.github.com');
  // only proceed once promise is resolved
  let data = await response.json();
  // only proceed once second promise is resolved

dusenberrymw

Spark Tips & Tricks

Misc. Tips & Tricks

• If values are integers in [0, 255], Parquet will automatically compress to use 1 byte unsigned integers, thus decreasing the size of saved DataFrame by a factor of 8.
• Partition DataFrames to have evenly-distributed, ~128MB partition sizes (empirical finding). Always err on the higher side w.r.t. number of partitions.
• Pay particular attention to the number of partitions when using flatMap, especially if the following operation will result in high memory usage. The flatMap op usually results in a DataFrame with a [much] larger number of rows, yet the number of partitions will remain the same. Thus, if a subsequent op causes a large expansion of memory usage (i.e. converting a DataFrame of indices to a DataFrame of large Vectors), the memory usage per partition may become too high. In this case, it is beneficial to repartition the output of flatMap to a number of partitions that will safely allow for appropriate partition memory sizes, based upon the