multinomial

the hat, shake it, select a number, and record the selected number as an attempt. Perform 300 attempts for this experiment and calculate the frequencies of 1, 2, and 3. Each time you do this experiment, you should expect a slightly different frequency distribution of the results, which reflects the variability of the sampling, while the distribution does not really deviate from the possible probability range. The following Multinomial class impleme

the results, which reflects the variability of the sampling, while the distribution does not really deviate from the possible probability range. The following Multinomial class implements this idea. You can use the following values to initialize the class: the number of labs to be tested, the number of attempts made in each experiment, and the number of options for each test. The results of each experiment are recorded in an array named Outcomes. L

occurrences. Each time you do this experiment, you should expect the results to have a slightly different frequency distribution, which reflects the variability of the sample, and this distribution does not really deviate from the possible probability range. The following Multinomial class implements this idea. You can initialize the class with the following values: The number of times to experiment, the number of attempts in each experiment, and t

= 3). Imagine putting numbers 1, 2, and 3 in your hat, shaking it, selecting a number, and recording the selected number as an attempt. Try the experiment 300 times and then calculate the frequency of 1, 2, and 3. Each time you do this experiment, you should expect the result to have a slightly different frequency distribution, which reflects the variability of the sample, and the distribution does not really deviate from the possible probability range. The following

the selected number as an attempt. Try the experiment 300 times, and then calculate the frequency of the 1, 2, and 3 occurrences. Each time you do this experiment, you should expect the results to have a slightly different frequency distribution, which reflects the variability of the sample, and this distribution does not really deviate from the possible probability range. The following Multinomial class implements this idea. You can initialize the

) The polynomial model (multinomial models) is the frequency type-the probability is calculated based on the number of occurrences of a word in the classification.It can be said that the polynomial model probability calculation is based on "word" as the base unit, set a text d= (T1,t2,..., tk), TK is the word in the document, allow repetition, then in the polynomial model, the class prior probability and the individual word class conditional probabili

(integer.tohexstring (Integer.parseint (string.valueof (CH), 2)); 　　　　　　　　　　　　 } return hex.tostring (); 　　　　　　 } //CRC codes main process public static int[] Makecrccodes (int[] sourcecodes, int[] multinomial) {//The lenght of CRC code is N bits longer than source code. The Codes //From 0 to Sourcelength are same as the source. N bits after source //are the CRC codes. N is decided by the multinomial.

L2 regularization, you will find that it is fitting. That is, when the prediction effect is poor, L1 regularization can be considered, and if the model has many characteristics, it is hoped that some unimportant characteristic coefficients will be zeroed, so that the model coefficients can be sparse, and the L1 regularization may also be used. L2 Liblinear Libniear only supports the OVR of multivariate logistic regression, and does not support MVM, but MVM is relatively accu

to generate the word W.Mathematical description of LDA modelFor the first physical process is obviously dirichlet-multinomial conjugate structureCompare the formula below (the formula is too lazy to knock, copy "Lda math gossip", is actually polynomial distribution in the Dirichlet distribution on the integral)We haveWhich indicates that the number of words in section M document K topic (that is, the number of occurrences of the K-topic throw, n is u

, SAG only use some of the sample gradient iteration, so when the sample size is not selected, and if the sample size is very large, such as greater than 100,000, SAG is the first choice. But SAG can not be used for L1 regularization, so when you have a large number of samples, but also need to L1 regularization of their own choice. Either by sampling the sample to reduce the sample size, or back to L2 regularization. In the official documentation for Sklearn, the use of Solver is described belo

multiple values, p (x|y) is subject to multiple distributions. When the value of x is continuous, the Y-value interval can be discretized, and then each interval classification is named as a specific value.2. Laplace smoothingIn a given training set, assume that X has a value of K {1,..., k}, so φi = P (z = i). In the case where Laplace smoothing is not used,And when a certain characteristic attributexnever appeared in the training set, then To avoid this situation, we use Laplace smoothing. Th

TopicModel-PLSA model and EM derivation of PLSA The PLSA model based on probability statistics uses the EM algorithm to learn model parameters. The probability graph model of PLSA is as follows: D indicates the document, Z indicates the implied category or topic, W indicates the observed word, indicating the probability that the word appears in the document, and the probability that the word appears under the topic in the document, specifies the probability that a topic appears a word. Each top

http://blog.csdn.net/pipisorry/article/details/42560877Based on the PLSA model of probability statistics, the EM algorithm is used to learn the model parameters.The probability map model of PLSA is as followswhere d represents the document, Z represents the implied category or topic, W is the observed word, indicates the probability of the word appearing in the document, the probability of the word appearing under the topic in the document, and the probability of the word appearing on the given

example of SVD dimensionality reduction given in IIR is as follows: On the left is the original matrix SVD decomposition, the right is only to retain the weight of the maximum 2 dimensions, the original matrix reduced to 2 dimensions after the situation. 2 pLSA Although the LSA based on SVD has achieved some success, it lacks rigorous mathematical statistical basis, and SVD decomposition is time-consuming. Hofmann on Sigir ' 99, the PLSA model based on probability statistics is proposed, and

1. Preface:Naive Bayes (naive Bayesian) is a simple multi-class classification algorithm, the premise of which is to assume that each feature is independent of each other . Naive Bayes training is mainly for each characteristic, under the condition of a given label, calculates the conditional probability of each characteristic under the condition of the label. Finally, the conditional probabilities after this training are used to predict.Because the version of Spark I'm using is 1.3.0. It contai

samples accounted for 0.8, test samples accounted for 0.2Val dataparts = Parseddatardd.randomsplit (Array (0.8, 0.2)) Val Trainrdd= Dataparts (0) Val Testrdd= Dataparts (1) //establish Bayesian classification model and trainVal model = Naivebayes.train (Trainrdd, lambda = 1.0, Modeltype = "Multinomial") //Test the test sampleVal Predictionandlabel = testrdd.map (p =(Model.predict (p.features), P.label, P.features)) Val showpredict= Predictionan

only guess by a large number of samples (and also with a priori knowledge), which is the reverse process. Typical discrete random variable distributions are: Bernoulli distribution (Bernoulli distribution) Two-item distribution (binomial distribution) classification distribution (categorical distribution) Multi-item distribution (multinomial distribution) And their conjugate prior distribution: Beta distribution (beta distribution) Dirichlet distribu

a particle from the old set. in the "Wheel" analogy, specified strated in the figure below, This method consists of picking n independent random directions. The name of this method comes from the fact that the probability mass function for the duplication countsNIIs the multinomial distribution with the weights as parameters. 2.2 Prresidual This method comprises of two stages. Firstly, participant are sampled deterministically by picking copies of

= fetch_20newsgroups_vectorized (subset = ' Test '); Print "The Shape of Train is" +repr (tfidf_train_3.data.shape) Print "The shape of Test is" +repr (tfidf_test_3.data.shape) Results: *************************Fetch_20newsgroups_vectorized*************************The shape of Train is (11314, 130107)The shape of test is (7532, 130107)3. Classification3.1 multinomial Naive Bayes ClassifierSee code comment, do not explain[Python]View Plaincopy

Multinomial distribution The corresponding multivariate distribution can be obtained by extending the two-yuan case of two-yuan distribution to multiple variables. First, the Bernoulli distribution is extended to the multivariate hypothesis for the discrete variable x x, there may be K K, then x x observation is represented as a vector, and satisfies ∑kk=1xk=1∑k=1kxk=1, only one dimension of the value of 1 1, the other is 0 0. So the probability mass