def __init__(self, params): super(%CLASS%, self).__init__(params) tmp = NuSVC() params = tmp.get_params() for key in params: self.create_new_output(type_="data", label=key, pos=-1) del tmp self.create_new_output(type_="data", label="param dict", pos=-1)
def __init__(self, params): super(NuSVCSetParams_NodeInstance, self).__init__(params) tmp = NuSVC() params = tmp.get_params() for key in params: self.create_new_input(type_="data", label=key, widget_name="std line edit m", widget_pos="besides", pos=-1) del tmp
''' kernel = ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable ''' ''' gamma = 'auto' or 'scale' ''' import numpy as np X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) y = np.array([1, 1, 2, 2]) from sklearn.svm import NuSVC # poly 多项式核函数 # degree [int, optional (default=3)]: Degree of the polynomial kernel function (‘poly’). # Ignored by all other kernels. clf = NuSVC(kernel='poly', degree=3, gamma='auto', nu=0.5, tol=0.001) clf.fit(X, y) print(clf.predict([[-0.8, -1]])) print(clf.get_params()) # rbf 径向基核函数 clf = NuSVC(kernel='rbf', gamma='scale', nu=0.5, tol=1e-3) clf.fit(X, y) print(clf.predict([[-0.8, -1]])) # sigmoid S型内核函数 clf = NuSVC(kernel='sigmoid', gamma='scale', tol=0.001) clf.fit(X, y) print(clf.predict([[-0.8, -1]]))
print 'LinearSVC precision test: {}'.format(lsvc_score_test) print '' lsvr = LinearSVR() print 'LinearSVR config:' print svc.get_params() lsvr.fit(smr_train.feature_matrix, smr_train.labels) lsvr_score_train = svc.score(smr_train.feature_matrix, smr_train.labels) print 'LinearSVR precision train: {}'.format(lsvr_score_train) lsvr_score_test = lsvr.score(smr_test.feature_matrix, smr_test.labels) print 'LinearSVR precision test: {}'.format(lsvr_score_test) print '' nusvc = NuSVC() print 'NuSVC config:' print nusvc.get_params() nusvc.fit(smr_train.feature_matrix, smr_train.labels) nusvc_score_train = nusvc.score(smr_train.feature_matrix, smr_train.labels) print 'NuSVC precision train: {}'.format(nusvc_score_train) nusvc_score_test = nusvc.score(smr_test.feature_matrix, smr_test.labels) print 'NuSVC precision test: {}'.format(nusvc_score_test) print '' nusvr = NuSVR() print 'NuSVR config:' print nusvr.get_params() nusvr.fit(smr_train.feature_matrix, smr_train.labels) nusvr_score_train = svc.score(smr_train.feature_matrix, smr_train.labels) print 'NuSVR precision train: {}'.format(nusvr_score_train) nusvr_score_test = nusvr.score(smr_test.feature_matrix, smr_test.labels) print 'NuSVR precision test: {}'.format(nusvr_score_test)