o*[!@sddlZddlZddlTddlmZdddddd geZejjejjegeje_d dZ d dZ d dZ d dZ dddd Z dddZdS)N)*)__all__ evaluationssvm_load_model svm_predictsvm_read_problemsvm_save_model svm_trainc Csg}g}xt|D]}|jdd}t|dkrP|dg7}|\}}i}x?|jD]1}|jd\}} t| |t| [y, x] Read LIBSVM-format data from data_file_name and return labels y and data instances x. N:)opensplitlenfloatint) Zdata_file_nameZprob_yZprob_xlinelabelfeaturesxieindvalrH/data/kushashwa/learnopencv/ImageMetrics/Python/libsvm/python/svmutil.pyrs  cCs=tj|j}|s-td|dSt|}|S)zc svm_load_model(model_file_name) -> model Load a LIBSVM model from model_file_name and return. zcan't open model file %sN)libsvmrencodeprint toPyModel)model_file_namemodelrrrr$s  cCstj|j|dS)ze svm_save_model(model_file_name, model) -> None Save a LIBSVM model to the file model_file_name. N)rrr)rr rrrr1sc Cs\t|t|kr$tdd}}d}}}}}xt||D]v\} } | | krv|d7}|| | | | 7}|| 7}|| 7}|| | 7}|| | 7}|| | 7}qTWt|} d|| } || } yF| |||| |||| |||| |||}Wntd}YnX| | |fS)z evaluations(ty, pv) -> (ACC, MSE, SCC) Calculate accuracy, mean squared error and squared correlation coefficient using the true values (ty) and predicted values (pv). zlen(ty) must equal to len(pv)rr gY@nan)r ValueErrorzipr)typvZ total_correctZ total_errorZsumvZsumyZsumvvZsumyyZsumvyvylACCMSESCCrrrr9s*        FcCsd \}}t|ttfr}t|ttfs<t|||}}}t|}t||d|jtk}n9t|tr|}t|tr|}n t|}|dks|dkrtd|jtkr^xr|j D]g}|dj |dj } } |dj dkr3t d| dksN| |j krt dqW|jdkr|j dkrd|j |_tj|jtj||} | rt d| |jr||j|j} } t| }tj||| |t|jd| |d| \}}}|jttgkrgtd |td ||Std ||Sn.tj||}t|}|j |_ |SdS) aP svm_train(y, x [, options]) -> model | ACC | MSE 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) NisKernelzWrong 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%%)NN) isinstancelisttupleAssertionError svm_parameter svm_problem kernel_type PRECOMPUTED TypeErrorx_spaceindexvaluer"ngammarsvm_set_print_string_function print_funcsvm_check_parametercross_validationr(nr_foldc_doublesvm_cross_validationrr'svm_type EPSILON_SVRNU_SVRrr r)arg1arg2arg3Zprobparamr'xoptionsridxrerr_msgr(r?targetr)r*r+mrrrr VsR%  !      /  r cCsdd}d}|j}d}xr|t|kr||dkrf|d7}t||}n%||dkrt}n td|d7}q'W|j}|j} |j} g} g} |r| std|tt gkr |d |j d} t | } xM|D]i}t |d |j jtk\}}tj||| }| d | }| |g7} | |g7} q!Wn| r|d |tt tfkrd}n| | dd }t |}x|D]}t |d |j jtk\}}tj|||}| dkr<dg}n|d |}| |g7} | |g7} qWt|| \}}}t|}|t tgkr|d||d|n%|d|t||d|f| |||f| fS)a svm_predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals) 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. cSst|dS)N)r)srrrinfoszsvm_predict..inforz-br z-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=%gr,Nz@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)rrr print_nullr" get_svm_typeis_probability_model get_nr_classrDrCget_svr_probabilityr@gen_svm_nodearrayrHr3r4rsvm_predict_probability ONE_CLASSNU_SVCsvm_predict_valuesr)r'rIrNrJrPZpredict_probabilityargvirB is_prob_modelnr_classZ pred_labelsZ pred_valuesZprob_estimatesrrKrvalues nr_classifier dec_valuesr)r*r+r(rrrrsh           $     $    %)ossyssvmrZsvm_allpathdirnameabspath__file__rrrrr rrrrrs    +   W