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@fawda123
fawda123 / gar_fun.r
Last active September 3, 2021 16:32
gar_fun
gar.fun<-function(out.var,mod.in,bar.plot=T,struct=NULL,x.lab=NULL,
y.lab=NULL, wts.only = F){
require(ggplot2)
require(plyr)
# function works with neural networks from neuralnet, nnet, and RSNNS package
# manual input vector of weights also okay
#sanity checks
@MLnick
MLnick / sklearn-lr-spark.py
Created February 4, 2013 14:29
SGD in Spark using Scikit-learn
import sys
from pyspark.context import SparkContext
from numpy import array, random as np_random
from sklearn import linear_model as lm
from sklearn.base import copy
N = 10000 # Number of data points
D = 10 # Numer of dimensions
ITERATIONS = 5
@pprett
pprett / grid_search.py
Created October 31, 2012 19:42
Parallel grid search for sklearn Gradient Boosting
"""Parallel grid search for sklearn's GradientBoosting.
This script uses IPython.parallel to run cross-validated
grid search on an IPython cluster. Each cell on the parameter grid
will be evaluated ``K`` times - results are stored in MongoDB.
The procedure tunes the number of trees ``n_estimators`` by averaging
the staged scores of the GBRT model averaged over all K folds.
You need an IPython ipcluster to connect to - for local use simply