#print(gridsearch.score(list(testData[:, 0]), testData[:,1].astype('int'))) #print(gridsearch.best_params_) print('got Vectors') model = LabelSpreading(kernel='rbf') params = {'gamma': [0.1, 1.0, 10.0, 30.0, 50.0, 80.0, 100.0, 300.0], 'max_iter': [10, 100, 1000], 'alpha': [0.2, 0.4, 0.6, 0.8]} scoreDict = {} for max_iter in params['max_iter']: model.max_iter = max_iter for alpha in params['alpha']: model.alpha = alpha for gamma in params['gamma']: model.gamma = gamma model.fit(list(data), list(unlab)) score = model.score(list(testData), list(testLabels)) print(score, ' gamma = ', gamma, ' max_iter = ', max_iter, ' alpha = ', alpha) if (score in scoreDict): scoreDict[score].append( 'gamma = ' + str(gamma) + ' max_iter = ' + str(max_iter) + ' alpha = ' + str(alpha)) else: scoreDict[score] = [ 'gamma = ' + str(gamma) + ' max_iter = ' + str(max_iter) + ' alpha = ' + str(alpha)] knnModel = LabelSpreading(kernel='knn') knnParams = {'n_neighbors': [1, 4, 9, 16], 'max_iter': [10, 100, 1000], 'alpha': [0.2, 0.4, 0.6, 0.8]} for max_iter in knnParams['max_iter']: model.max_iter = max_iter for alpha in knnParams['alpha']: