logistic regression jmp

Unlike linear regression, instead of multiplying each feature directly by its coefficients, it uses an S-type function (the logistic function). As follows:The reason for using this form function (probability, derivation).The cost function, also not the sum of squared errors in linear regression, is based on the logarithmic likelihood function, as follows:The post

I was excited when I saw this part of the content, because it was finally linked to the theoretical content of my previous studies, which is part of the code implementation of the previous logistic regression theory, so if something that is not quite understood can be returned to the theoretical part to understand, Below we enter the topic----Logistic regressionF

The neural network can be seen in two ways, one is the set of layers, the array of layers, and the other is the set of neurons, which is the graph composed of neuron.In a neuron-based implementation, you need to define two classes of Neuron, WeightAn instance of the neuron class is equivalent to a vertex,weight consisting of a linked list equivalent to an adjacency table and a inverse adjacency table.In the layer-based implementation, each layer corresponds to a layer class, namely Logisticregre

#-*-Coding:utf-8-*-Import NumPyDef loaddataset ():Return Datamat,labelmatdef sigmoid (InX):Return 1.0/(1+numpy.exp (-inx))def gradascent (datamatin,classlabels):Datamatrix=numpy.mat (Damamatin)Labelmat=numpy.mat (Classlabels). Transpose ()#上升梯度alpha=0.01#迭代次数maxcycles=500#初始回归向量M,n=numpy.shape (Datamatrix)Weights=numpy.ones ((n,1))for k in range (Maxcycles):H=sigmoid (datamatrix*weights)Error= (labelmat-h)Weights=weights+alpha*datamatrix.transpose () *errorPassReturn weightsdef test ():Dataarr,l

1.sensitivity, also known as recall,true positive rate, means that the predicted positive case is proportional to (true positive) and all facts are positive.2.specificity, also called, true negative rate, meaning is the proportion of case that is predicted to be negative (true negative) and all facts are negative.3.roc (receiver operating characteristic)ROC can be used to evaluate the classification algorithms of two classifications.The longitudinal axis of the ROC curve is the ratio of sensitiv

Data preparation:A set of stock history sold data (stock code: 601106 China One heavy), starting Date: 2011-01-04 to date, where variables are "open", "highest", "minimum", "close", "Total hand", "Amount", "ups and downs" and so onUPDATEFactstockSET [Ups and Downs] =N'Rise'WHERE [gains] > 0UPDATEFactstockSET [Ups and Downs] =N'Fall'WHERE [gains] 0UPDATEFactstockSET [Ups and Downs] =N'Flat'WHERE [gains] = 0SELECT [Ups and Downs] , COUNT(*) asCnt fromFactstockGROUP by [

] = Data.flatmap (_ (1). Split (";")). Map (_.split (":") (0)). Distinct (). zipwiThindex (). Collectasmap ()//Build the training dataset, where sample represents a sample containing tags and feature val traindata:rdd[labeledpoint] = Data.map (sample=> {//Because Mllib only receives 1.0 and 0.0 to classify, here we pattern match, turn into 1.0 and 0.0 val label = sample (0) Match {case "1" => 1.0 case _ => 0.0}//Find a non-0 element subscript, look up subscript in dictionary map with the charact

monotonically increasing function , so when the logarithm function takes the maximum value, the original function also obtains the maximum value. (Logarithmic function Y=logax y=\log_a{x}, monotonically incrementing when A>1 a>1, and monotonically decreasing when 0 (3) the derivative is 0 to obtain the likelihood equation; (4) to solve the likelihood equation, get The parameters are the desired, and the The below uses the maximum likelihood method to estimate the parameters in

Logistic regression is a classification method, which is used for two classification problems, and its basic idea is: Look for the appropriate hypothesis function, the classification function, to predict the results of the input data; The structure loss function is used to indicate the deviation between the predicted output and the actual classes in the training data; Minimize the loss function

([0,length/100],[-theta[0],-theta[0]-theta[1]*length/100]/theta[2]) the plt.show ()117length=200118Iter=5119x,y=Datan (length) - 121theta,cost=BFGS (x,y,iter)122 PrintTheta#[[ -18.93768161][-16.52178427][16.95779981]]123 PrintTesT (Theta, Np.mat (x), y)#0.935124 showp (X,y,theta.geta (), Cost,iter) the 126theta,cost=DFP (x,y,iter)127 PrintTheta#[[ -18.51841028][-16.17880599][16.59649161]] - PrintTesT (Theta, Np.mat (x), y)#0.935129 showp (X,y,theta.geta (), Cost,iter) the 131theta,cost=Newtonme

http://blog.csdn.net/pipisorry/article/details/43884027Machine learning machines Learning-andrew NG Courses Study notesClassification0, 1 meansDenote with 0 is the negative classDenote with 1 is the positive class.Hypothesis representationDecision BoundaryCost FunctionSimplified cost Function and Gradient descentAdvanced optimizationMulticlass Classification-one-vs-allfrom:http://blog.csdn.net/pipisorry/article/details/43884027Machine Learning-vi. Logistic