B VNbm*@sddlZddlZejejegeje_ddlTddlmZddlm Z m Z ddl Tddl mZ ej ddkreZddlmZdd Znd d Zd d d dgee Zdd Zdd ZdddZddd ZdS)N)*)__all__)scipysparse)izipcCst|tr|dSt|S)Nzutf-8) isinstanceZunicodeencodestr)sr -lib/python3.7/site-packages/libsvm/svmutil.pyrcCs t|dS)Nzutf-8)bytes)r r r rrrsvm_load_model svm_predictsvm_save_model svm_traincCs.tt|}|s"td|dSt|}|S)zl svm_load_model(model_file_name) -> model Load a LIBSVM model from model_file_name and return. zcan't open model file %sN)libsvmr_cstrprint toPyModel)model_file_namemodelr r rrs  cCstt||dS)zn svm_save_model(model_file_name, model) -> None Save a LIBSVM model to the file model_file_name. N)rrr)rrr r rr"scCs*d\}}t|ttfs&tr|t|tjr|t|ttfsNtrJt|tjtjfsNt|||}}}t|}t |||j t kd}n&t|t r|}t|tr|}nt|}|dks|dkrt d|j t kr(x`t |jD]R}|j|} | dj| dj} } | dkrtd| dks| |jkrtdqW|jdkrL|jdkrLd|j|_t|jt||} | rvtd | |jr|j|j} }t| }t||||t|jd| |d| \}}}|jt t!gkrt"d |t"d ||St"d ||Sn t#||}t$|}|j%|_%|SdS) a svm_train(y, x [, options]) -> model | ACC | MSE y: a list/tuple/ndarray of l true labels (type must be int/double). x: 1. a list/tuple of l training instances. Feature vector of each training instance is a list/tuple or dictionary. 2. an l * n numpy ndarray or scipy spmatrix (n: number of features). svm_train(prob [, options]) -> model | ACC | MSE svm_train(prob, param) -> model | ACC| MSE Train an SVM model from data (y, x) or an svm_problem prob using 'options' or an svm_parameter param. If '-v' is specified in 'options' (i.e., cross validation) either accuracy (ACC) or mean-squared error (MSE) is returned. options: -s svm_type : set type of SVM (default 0) 0 -- C-SVC (multi-class classification) 1 -- nu-SVC (multi-class classification) 2 -- one-class SVM 3 -- epsilon-SVR (regression) 4 -- nu-SVR (regression) -t kernel_type : set type of kernel function (default 2) 0 -- linear: u'*v 1 -- polynomial: (gamma*u'*v + coef0)^degree 2 -- radial basis function: exp(-gamma*|u-v|^2) 3 -- sigmoid: tanh(gamma*u'*v + coef0) 4 -- precomputed kernel (kernel values in training_set_file) -d degree : set degree in kernel function (default 3) -g gamma : set gamma in kernel function (default 1/num_features) -r coef0 : set coef0 in kernel function (default 0) -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1) -m cachesize : set cache memory size in MB (default 100) -e epsilon : set tolerance of termination criterion (default 0.001) -h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1) -b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0) -wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1) -v n: n-fold cross validation mode -q : quiet mode (no outputs) )NN)isKernelNzWrong types for the argumentsrz?Wrong input format: first column must be 0:sample_serial_numberz5Wrong input format: sample_serial_number out of rangeg?z Error: %sz(Cross Validation Mean squared error = %gz5Cross Validation Squared correlation coefficient = %gz Cross Validation Accuracy = %g%%)&r listtuplerndarrayrspmatrixAssertionErrorZ svm_parameterZ svm_problem kernel_type PRECOMPUTED TypeErrorrangelxindexvalue ValueErrornZgammarZsvm_set_print_string_functionZ print_funcZsvm_check_parameterZcross_validationnr_foldc_doubleZsvm_cross_validation evaluationsysvm_type EPSILON_SVRNU_SVRrrrZx_space)Zarg1Zarg2Zarg3Zprobparamr/r'optionsixiidxvalZerr_msgr&r,targetACCMSESCCmr r rr*sT-(           "    cCsdd}tr$t|tjr$t|}n:tr>t|tjr>|}n t|ttfs^t d t |t|ttfstr|t|tjst d t |d}| }d}xT|t |kr||dkr|d7}t||}n||dkrt}ntd |d7}qW|}|} |} g} g} tr4t|tjr4|jd} nt |} |r6| sPtd |ttgkrr|d |d} t| }xt| D]}trt|tjrt|j||j|d}t|j||j|f|jj t!kd \}}nt|||jj t!kd \}}t"#|||}|d | }| |g7} | |g7} qWn| rD|d|t$tt%fkrZd}n| | dd}t|}xt| D]}trt|tjrt|j||j|d}t|j||j|f|jj t!kd \}}nt|||jj t!kd \}}t"&|||}| dkrdg}n |d |}| |g7} | |g7} q~Wt |dkrTdg| }t'|| \}}}|ttgkr|d||d|n"|d|tt(| |d| f| |||f| fS)a svm_predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals) y: a list/tuple/ndarray of l true labels (type must be int/double). It is used for calculating the accuracy. Use [] if true labels are unavailable. x: 1. a list/tuple of l training instances. Feature vector of each training instance is a list/tuple or dictionary. 2. an l * n numpy ndarray or scipy spmatrix (n: number of features). Predict data (y, x) with the SVM model m. options: -b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported. -q : quiet mode (no outputs). The return tuple contains p_labels: a list of predicted labels p_acc: a tuple including accuracy (for classification), mean-squared error, and squared correlation coefficient (for regression). p_vals: a list of decision values or probability estimates (if '-b 1' is specified). If k is the number of classes, for decision values, each element includes results of predicting k(k-1)/2 binary-class SVMs. For probabilities, each element contains k values indicating the probability that the testing instance is in each class. Note that the order of classes here is the same as 'model.label' field in the model structure. cSs t|dS)N)r)r r r rinfoszsvm_predict..infoz type of x: {0} is not supported!z type of y: {0} is not supported!rz-brz-qz Wrong optionsz+Model does not support probabiliy estimateszwProb. model for test data: target value = predicted value + z, z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g)rNz@Model supports probability estimates, but disabled in predicton.z$Mean squared error = %g (regression)z1Squared correlation coefficient = %g (regression)z(Accuracy = %g%% (%d/%d) (classification)d))rr rZascontiguousarrayrr Ztocsrrrr$formattypesplitlenintZ print_nullr*Z get_svm_typeZis_probability_modelZ get_nr_classshaper2r1Zget_svr_probabilityr-r%sliceZindptrZgen_svm_nodearrayindicesdatar3r"r#rZsvm_predict_probabilityZ ONE_CLASSZNU_SVCZsvm_predict_valuesr.round)r/r'r=r4r?Zpredict_probabilityargvr5r0Z is_prob_modelZnr_classZ pred_labelsZ pred_valuesZ nr_instanceZprob_estimatesZindslicer6r7ZlabelvaluesZ nr_classifierZ dec_valuesr:r;r<r r rrs         *   *     ")NN)r>)ossyspathdirnameabspath__file__ZsvmrZsvm_allrrZ commonutilZ common_all version_infoZxranger% itertoolsrziprrrrrr r r rs       `