def main():
hsic_lasso = HSICLasso()
hsic_lasso.input("../tests/test_data/csv_data_mv.csv",
output_list=['output1', 'output2'])
hsic_lasso.regression(5)
hsic_lasso.dump()
hsic_lasso.plot_path()
def main():
#Numpy array input example
hsic_lasso = HSICLasso()
data = sio.loadmat("../tests/test_data/matlab_data.mat")
X = data['X'].transpose()
Y = data['Y'][0]
featname = ['Feat%d' % x for x in range(1, X.shape[1] + 1)]
hsic_lasso.input(X, Y, featname=featname)
hsic_lasso.regression(5)
hsic_lasso.dump()
hsic_lasso.plot_path()
#Save parameters
hsic_lasso.save_param()
def main():
hsic_lasso = HSICLasso()
#out_list = ['c'+str(i) for i in range(1,51)]
#print (out_list)
hsic_lasso.input("./user_data_new.csv",
output_list=[
'c1', 'c2', 'c3', 'c4', 'c5,', 'c6', 'c7', 'c8', 'c9',
'c10'
])
# ,'c11', 'c12', 'c13', 'c14', 'c15,', 'c16', 'c17', 'c18', 'c19', 'c20','c21', 'c22', 'c23', 'c24', 'c25,', 'c26', 'c27', 'c28', 'c29', 'c30'])
hsic_lasso.regression(100, B=50)
hsic_lasso.dump()
select_index = hsic_lasso.get_index()
print(select_index)
print(hsic_lasso.get_index_score())
#hsic_lasso.plot_path()
print(hsic_lasso.get_features())
X_select = hsic_lasso.X_in[select_index, :]
np.savetxt('X_select.txt', X_select, fmt=str('%.5f'), encoding='utf-8')
from pyHSICLasso import HSICLasso
hsic_lasso = HSICLasso()
hsic_lasso.input("SNR-26415.csv")
print(hsic_lasso.classification(100))
hsic_lasso.get_features()
l = []
l.append(hsic_lasso.get_features())
print(hsic_lasso.get_features())
print(len(l))
temp = 0
hsic_lasso.dump()
for i in range(0, len(l)):
print(l[i])
temp = temp + 1
print(temp)
def main():
hsic_lasso = HSICLasso()
hsic_lasso.input("../tests/test_data/matlab_data.mat")
hsic_lasso.regression(5)
hsic_lasso.dump()
hsic_lasso.plot_path()
