def generate_report_nipbernoulligm(self, mdl, t_train, x0_train, t_test,
x0_test):
if (mdl.is_fitted == True):
t_all = np.concatenate((t_train, t_test), axis=0)
x_all = np.concatenate((x0_train, x0_test), axis=0)
x_pred = mdl.predict(t_all)
lens = len(mdl.x_orig)
err = x_pred[:lens] - mdl.x_orig
m = len(t_train)
x0_train = x_all[:m]
x0_test = x_all[m - 1:]
t_train = t_all[:m]
t_test = t_all[m - 1:]
rmse = np.sqrt(sum((err)**2 / lens))
rmse = np.round(rmse, 4)
t3 = np.arange(lens) + 1
p = plt.figure()
plt.subplot(2, 1, 1)
plt.plot(t_train, x0_train)
plt.plot(t_train, x_pred[:m])
plt.plot(t_test, x0_test)
plt.plot(t_test, x_pred[m - 1:lens])
plt.axvline(x=m, ls="--", lw=1, c='k')
plt.legend([
'x_orig_models', 'x_pred_models', 'x_orig_detected',
'x_pred_detected'
])
plt.axvline(x=m, ls="--", lw=1, c='k')
plt.subplot(2, 1, 2)
plt.stem(t3, err, linefmt="c:", markerfmt="o", basefmt="r-")
plt.axvline(x=m, ls="--", lw=1, c='k')
# 打印报告
x1 = NewInformationPriorityAccumulation.nipago(mdl.x_orig, mdl.r)
x1_pred = NewInformationPriorityAccumulation.nipago(x_pred, mdl.r)
model_report = pd.DataFrame({
'x_orig': mdl.x_orig,
'x1': x1,
'x1_pred': x1_pred,
'x_pred': x_pred,
'error': err,
})
# 公式表达
eq = generate_report.formula_expression_bernoulligm(None, mdl)
c1 = [p, model_report, eq]
return c1
else:
t = np.concatenate((t_train, t_test), axis=1)
x0 = np.concatenate((x0_train, x0_test), axis=1)
mdl.fit(t, x0)
generate_report.generate_report_nipbernoulligm(
mdl, t_train, x0_train, t_test, x0_test)
def generate_report(self, mdl, y_train, t_x_train, y_test, t_x_test, **kwargs):
if (mdl.is_fitted == True):
y_all = np.concatenate((y_train, y_test), axis=0)
t_x = np.concatenate((t_x_train, t_x_test), axis=0)
y_pred = mdl.predict(t_x)
t_all = t_x[0:, 0]
lens = len(mdl.x_orig)
m = len(y_train)
err = y_pred[:lens] - mdl.x_orig
y0_train = y_all[:m]
y0_test = y_all[m-1:]
t_train = t_all[:m]
t_test = t_all[m - 1:]
rmse = np.sqrt(sum((err) ** 2 / lens))
rmse = np.round(rmse, 4)
t3 = np.arange(lens) + 1
p = plt.figure()
plt.subplot(2, 1, 1)
plt.plot(t_train, y0_train)
plt.plot(t_train, y_pred[:m])
plt.plot(t_test, y0_test)
plt.plot(t_test, y_pred[m - 1:lens])
plt.axvline(x=m, ls="--", lw=1, c='k')
plt.legend(['x_train', 'x_pred_train', 'x_test', 'x_pred_test'])
plt.axvline(x=m, ls="--", lw=1, c='k')
plt.title('Model and Test')
plt.subplot(2, 1, 2)
plt.stem(t3, err, linefmt="c:", markerfmt="o", basefmt="r-")
plt.title('RMSE = {}'.format(rmse))
plt.axvline(x=m, ls="--", lw=1, c='k')
y1 = NewInformationPriorityAccumulation.nipago(y_all, mdl.r)
y1_pred = NewInformationPriorityAccumulation.nipago(y_pred, mdl.r)
model_report = pd.DataFrame({
'x_orig': mdl.x_orig,
'x1': y1,
'x1_pred': y1_pred,
'x_pred': y_pred,
'error': err,
})
c1 = [p, model_report]
return c1
else:
y = np.concatenate((y_train, y_test), axis=0)
t_x = np.concatenate((t_x_train, t_x_test), axis=0)
mdl.fit(y, t_x)
Generate_report_nipagon.generate_report(None, mdl, y_train, t_x_train, y_test, t_x_test)
def predict(self, t_x):
all_t = t_x[0:, 0]
x1 = t_x[0:, 1:]
x1_pred = _res_func_list.res_funcs[self.mdl_name].compute(
self.params, all_t, x1)
x_pred = NewInformationPriorityAccumulation.nipago(x1_pred, -self.r)
return x_pred
def fit(self, y):
x1 = NewInformationPriorityAccumulation.nipago(y, self.r)
z1 = ModelMethods.get_backvalue(x1)
z1_square = np.power(z1, self.n)
B = ModelMethods.construct_matrix(z1, z1_square)
self.x_orig = y
self.params = ModelMethods.get_params(B, y)
return self
def fit(self, x, y):
x1 = NewInformationPriorityAccumulation.nipago(y, self.r)
z1 = ModelMethods.based(x1)
ones_array = np.diff(x).astype(np.float64)
ones_array = ones_array.reshape([-1, 1])
B = ModelMethods.construct_matrix(-z1, ones_array)
self.x_orig = y
self.params = ModelMethods.get_params(B, x1)
return self
def fit(self, x, y):
x1 = NewInformationPriorityAccumulation.nipago(y, self.r)
z1 = ModelMethods.get_backvalue(x1)
arange_array = x[1:]
arange_array = arange_array.reshape([-1, 1])
B = ModelMethods.construct_matrix(z1, arange_array)
self.x_orig = y
self.params = ModelMethods.get_params(B, y)
return self
def fit(self, x, y):
lens_1 = len(y[0, 0:])
y1 = y.T
x1 = np.zeros(y1.shape)
for i in range(0, lens_1):
x1[i, 0:] = NewInformationPriorityAccumulation.nipago(
y1[i, 0:], self.r)
x1 = x1.T
x1_0 = x1[0:, 0]
z1 = ModelMethods.get_backvalue(x1_0)
n_array = x1[1:, 1:]
B = ModelMethods.construct_matrix(z1, n_array)
self.x_orig = y
self.params = ModelMethods.get_params(B, np.array(y)[0:, 0])
self.x1 = x1
return x1
def fit(self, y, x):
x_train = x[0:, 1:]
y_conect = np.concatenate((y, x_train), axis=1)
lens_1 = len(y_conect[0, 0:])
y1 = y_conect.T
x1 = np.zeros(y1.shape)
for i in range(0, lens_1):
x1[i, 0:] = NewInformationPriorityAccumulation.nipago(
y1[i, 0:], self.r)
x1 = x1.T
x1_0 = x1[0:, 0]
z1 = ModelMethods.get_backvalue(x1_0)
n_array = x1[1:, 1:]
B = ModelMethods.construct_matrix(z1, n_array)
self.x_orig = y
self.params = ModelMethods.get_params(B, np.array(y)[0:, 0])
self.x1 = x1
return x1
def fit(self, x, y):
lens_1 = len(y[0, 0:])
y1 = y.T
x1 = np.zeros(y1.shape)
for i in range(0, lens_1):
x1[i, 0:] = NewInformationPriorityAccumulation.nipago(
y1[i, 0:], self.r)
x1 = x1.T
ones_array = np.diff(x).astype(np.float64)
ones_array = ones_array.reshape([-1, 1])
x1_0 = x1[0:-1, 0]
x1_0 = x1_0.reshape([-1, 1])
x1_n = x1[1:, 1:]
B_x = ModelMethods.construct_matrix(-x1_0, x1_n)
B = ModelMethods.construct_matrix(-B_x, ones_array)
self.x_orig = y
self.params = ModelMethods.get_params(B, np.array(y)[0:, 0])
self.x1 = x1
return x1
def predict(self, t):
all_t = np.arange(0, np.max(t))
x1_pred = _res_func_list.res_funcs[self.mdl_name].compute(
self.params, all_t, self.x_orig[0])
x_pred = NewInformationPriorityAccumulation.nipago(x1_pred, -self.r)
return x_pred