def main(): kernelFile = '/afs/cs.stanford.edu/u/rwitten/projects/multi_kernel_spl/data/allkernels_info.txt' trainFile = './train.newsmall_1_reducedy.txt' #TODO: Rafi, move this to your data directory so it doesn't clutter things up params = Params() spl_params = Params() spl_params.spl_mode = 0 params.max_outer_iter = 1337 #TODO: make this user input loadKernelFile(kernelFile, params) loadTrainFile(trainFile, params) w = ImagePsi.PsiObject(params) LSSVM.optimize(w, params, spl_params) return params
def train_and_predict_task(self, t, train_X, train_y, eval_X, param_dict): kernel_func = self.get_kernel_func(param_dict['kernel'], param_dict['beta']) self.models[t] = lssvm.LSSVM(C=param_dict['C'], kernel_func=kernel_func) converged = self.models[t].fit(train_X, train_y) if converged: preds = self.models[t].predict(eval_X) else: # predict majority class preds = np.sign(np.mean(train_y)) * np.ones(len(eval_X)) return preds
def main(): try: params = UserInput.getUserInput('train') ExampleLoader.loadExamples(params) CommonApp.setExampleCosts(params) w = None if params.initialModelFile: w = CacheObj.loadObject(params.initialModelFile) else: w = CommonApp.PsiObject(params,False) globalSPLVars = SPLSelector.SPLVar() if params.splParams.splMode != 'CCCP': SPLSelector.setupSPL(params) w = LSSVM.optimize(w, globalSPLVars, params) CacheObj.cacheObject(params.modelFile,w) Performance.printStrongAndWeakTrainError(params, w) except Exception, e : import traceback traceback.print_exc(file=sys.stdout)
Test = numpy.loadtxt('dataset///monks_2_test.txt') X_t = Test[:,1:-1] Y_t = Test[:,0] X_N_t = NorX.fT(X_t) Y_N_t = NorY.fT(Y_t) Y_N_t = Y_N_t * 2 -1 (alpha,b,K) = LSSVM.LSSVM_CV(X_N,Y_N,'RBF',[0.01,0.1,0.5,1,2,10,25,50,100],[0.001,0.01,0.1,0.2,0.5,1,5,10,40,100],arg2 = None) #(alpha,b,K) = LSSVM.LSSVM_CV(X_N,Y_N,'LINEAR',[0.001,0.005,0.01,0.05,0.1,0.5,1,4,10,25,100]) #(alpha,b,K) = LSSVM.LSSVM_CV(X_N,Y_N,'POLY',[0.01,0.1,0.5,1,2,10,25,50,100],[0.001,0.01,0.1,0.2,0.5,1,5,10,40,100],[1,2,3,4,5]) #(alpha,b,K) = LSSVM.LSSVM_CV(X_N,Y_N,'TANH',[0.01,0.1,0.5,1,2,10,25,50,100],[-10,-5,-3,-2,-1,-0.5,-0.1,-0.05,-0.01,0.01,0.05,0.1,0.5,1,3,5,10],[0.1,1,2,3,10]) #(alpha,b,K) = LSSVM.LSSVM_CV(X_N,Y_N,'TL1',[0.001,0.005,0.01,0.03,0.1,0.5,1,2,10,25,50,100],[0.001,0.005,0.01,0.05,0.1,0.2,0.5,1,2,5,10,20]) Y_predict = LSSVM._LSSVMpredict(X_N_t,K,alpha,b,Y_N) acc = LSSVM._compare(Y_N_t,Y_predict) print(acc)