bayesian

Bayesian Rating

由 旧时模样 提交于 2019-12-05 02:33:15
问题 $avg_num_votes = 18; // Average number of votes in all products $avg_rating = 3.7; // Average rating for all products $this_num_votes = 6; // Number of votes for this product $this_rating = 4; // Rating for this product $bayesian_rating = ( ($avg_num_votes * $avg_rating) + ($this_num_votes * $this_rating) ) / ($avg_num_votes + $this_num_votes); echo round($bayesian_rating); // 3 What is the significance of 3? What is the highest possible rating? 回答1: you're comparing the ratings for this

How to report with APA style a Bayesian Linear (Mixed) Models using rstanarm?

由 守給你的承諾、 提交于 2019-12-05 02:27:59
问题 I'm currently struggling with how to report, following APA-6 recommendations, the output of rstanarm::stan_lmer() . First, I'll fit a mixed model within the frequentist approach, then will try to do the same using the bayesian framework. Here's the reproducible code to get the data: library(tidyverse) library(neuropsychology) library(rstanarm) library(lmerTest) df <- neuropsychology::personality %>% select(Study_Level, Sex, Negative_Affect) %>% mutate(Study_Level=as.factor(Study_Level),

Python NLTK not sentiment calculate correct

由 杀马特。学长 韩版系。学妹 提交于 2019-12-04 19:19:48
I do have some positive and negative sentence. I want very simple to use Python NLTK to train a NaiveBayesClassifier for investigate sentiment for other sentence. I try to use this code, but my result is always positive. http://www.sjwhitworth.com/sentiment-analysis-in-python-using-nltk/ I am very new at python so there my be a mistake in the code when i copy it. import nltk import math import re import sys import os import codecs reload(sys) sys.setdefaultencoding('utf-8') from nltk.corpus import stopwords __location__ = os.path.realpath( os.path.join(os.getcwd(), os.path.dirname(__file__)))

How do I add limiting conditions when using GpyOpt?

由 假装没事ソ 提交于 2019-12-04 16:50:36
Currently I try to minimize the function and get optimized parameters using GPyOpt. import GPy import GPyOpt from math import log def f(x): x0,x1,x2,x3,x4,x5 = x[:,0],x[:,1],x[:,2],x[:,3],x[:,4],x[:,5], f0 = 0.2 * log(x0) f1 = 0.3 * log(x1) f2 = 0.4 * log(x2) f3 = 0.2 * log(x3) f4 = 0.5 * log(x4) f5 = 0.2 * log(x5) return -(f0 + f1 + f2 + f3 + f4 + f5) bounds = [ {'name': 'x0', 'type': 'discrete', 'domain': (1,1000000)}, {'name': 'x1', 'type': 'discrete', 'domain': (1,1000000)}, {'name': 'x2', 'type': 'discrete', 'domain': (1,1000000)}, {'name': 'x3', 'type': 'discrete', 'domain': (1,1000000)}

Any framework for real-time correlation/analysis of event-stream (aka CEP) in Erlang?

由 放肆的年华 提交于 2019-12-04 14:01:33
问题 Would like to analyze a stream of events, sharing certain characteristics (s.a. a common source), and within a given time-window, ultimately to correlate those multiple events and draw some inference from same, and finally launch some action. My limited knowledge of Complex-Event-Processing (CEP) tells me that, it is the ideal candidate for such things. However in my research so far I found people compare that with Rule-Engines, and Bayesian Classifier, and sometimes using a combination of

How to implement TF_IDF feature weighting with Naive Bayes

由 随声附和 提交于 2019-12-04 13:00:20
I'm trying to implement the naive Bayes classifier for sentiment analysis. I plan to use the TF-IDF weighting measure. I'm just a little stuck now. NB generally uses the word(feature) frequency to find the maximum likelihood. So how do I introduce the TF-IDF weighting measure in naive Bayes? You can visit the following blog shows in detail how do you calculate TFIDF. You use the TF-IDF weights as features/predictors in your statistical model. I suggest to use either gensim [1]or scikit-learn [2] to compute the weights, which you then pass to your Naive Bayes fitting procedure. The scikit-learn

MCMCglmm multinomial model in R

由 ℡╲_俬逩灬. 提交于 2019-12-04 11:49:04
问题 I'm trying to create a model using the MCMCglmm package in R. The data are structured as follows, where dyad, focal, other are all random effects, predict1-2 are predictor variables, and response 1-5 are outcome variables that capture # of observed behaviors of different subtypes: dyad focal other r present village resp1 resp2 resp3 resp4 resp5 1 10101 14302 0.5 3 1 0 0 4 0 5 2 10405 11301 0.0 5 0 0 0 1 0 1 … So a model with only one outcome (teaching) is as follows: prior_overdisp_i <- list

Fit a bayesian linear regression and predict unobservable values

由 有些话、适合烂在心里 提交于 2019-12-04 04:49:33
问题 I'd like to use Jags plus R to adjust a linear model with observable quantities, and make inference about unobservable ones. I found lots of example on the internet about how to adjust the model, but nothing on how to extrapolate its coefficients after having fitted the model in the Jags environment. So, I'll appreciate any help on this. My data looks like the following: ngroups <- 2 group <- 1:ngroups nobs <- 100 dta <- data.frame(group=rep(group,each=nobs),y=rnorm(nobs*ngroups),x=runif(nobs

Naive Bayes: the within-class variance in each feature of TRAINING must be positive

由 柔情痞子 提交于 2019-12-04 04:03:24
When trying to fit Naive Bayes: training_data = sample; % target_class = K8; # train model nb = NaiveBayes.fit(training_data, target_class); # prediction y = nb.predict(cluster3); I get an error: ??? Error using ==> NaiveBayes.fit>gaussianFit at 535 The within-class variance in each feature of TRAINING must be positive. The within-class variance in feature 2 5 6 in class normal. are not positive. Error in ==> NaiveBayes.fit at 498 obj = gaussianFit(obj, training, gindex); Can anyone shed light on this and how to solve it? Note that I have read a similar post here but I am not sure what to do?

What is a relatively simple way to determine the probability that a sentence is in English?

由 家住魔仙堡 提交于 2019-12-04 03:51:24
I have a number of strings (collections of characters) that represent sentences in different languages, say: Hello, my name is George. Das brot ist gut. ... etc. I want to assign each of them scores (from 0 .. 1) indicating the likelihood that they are English sentences. Is there an accepted algorithm (or Python library) from which to do this? Note: I don't care if the grammar of the English sentence is perfect. A bayesian classifier would be a good choice for this task: >>> from reverend.thomas import Bayes >>> g = Bayes() # guesser >>> g.train('french','La souris est rentrée dans son trou.'
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