def main():
data = dict(x=[[], []], y=[[], []])
val_data = dict(x=[[], []], y=[[], []])
Pred_True = [0] * class_number
pred_num=[0,0]
L = [0, 0]
last_j=float('-inf')
datas, L[0], L[1], Lall = pd.read_datas(file_name)
data_block = pd.split_datas(datas, cross_validation_number)
# i = 0
for cla, val in data_block:
# print(i)
# i += 1
for j in range(len(cla)):
(data['x'][j], data['y'][j], val_data['x'][j], val_data['y'][j]) = (
cla[j][:, :-1], cla[j][:, -1:], val[j][:, :-1], val[j][:, -1:])
myfisher = Fisher(L[0], L[1], Lall, data)
W = myfisher.W_Direction() # 投影参数
J=Eval.simple_Fisher_Friterion(W,myfisher.between_class_scatter_matrix())#评估一下投影效果，并更新投影向量
if J>last_j:
last_j=J
W_great=W
W0 = myfisher.OneKey_W0(W) # 阈值
# %%
for j in range(class_number):
predict_y = myfisher.Pred_result(W, val_data['x'][j], W0)
# print(predict_y)
pred_num[j]+=len(val_data['x'][j])
Pred_True[j] += Eval.Comp_as1(predict_y, val_data['y'][j][0][0])
# if j==1:
# print(Eval.Comp_as1(predict_y, val_data['y'][j][0][0]))
# print(W,W0)
# print(S_W)
# print(time.time()-t0)
# break
print(' 预测 第1类 第2类 全部')
for j in range(class_number):
print('实际第{0}类 {1} {2} {3}'.format(j+1,Pred_True[j],pred_num[j]-Pred_True[j],pred_num[j]))
print('mixed_accuracy：%f' % ((Pred_True[0]+pred_num[1]-Pred_True[1])/(pred_num[0]+pred_num[1])))
# print(os.path.isfile(Eval.w_filename),W_great)
# if os.path.isfile(Eval.w_filename)==False and W_great:
print('评价函数最大的W保存在',os.path.abspath(Eval.w_filename))
Eval.save_par(W_great)
pass
