import numpy as np rows = int(input("输入行数：")) cols = int(input("输入列数：")) # 输入数据元素 data = [] for i in range(rows): row = [] for j in range(cols): value = float(input(f"输入元素[{i}, {j}]: ")) row.append(value) data.append(row) X0 = data m, n = X0.shape X1 = np.cumsum(X0) X1 = np.cumsum(X0) X2 = np.zeros((n-2, n)) for i in range(1, n-1): X2[i-1, i:] = X1[i] + X1[i+1] B = -0.5 * X2 t = np.ones((n-1, 1)) B = np.concatenate((B, t), axis=1) YN = X0[1:] P_t = YN / X1[:n-1] A = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), YN.T) a = A[0] u = A[1] c = u / a b = X0[0] - c X = str(b) + 'exp(' + str(-a) + 'k)' + str(c) equation = 'X(k+1)=' + X print(equation) k = np.arange(len(X0)) Y_k_1 = b * np.exp(-a * k) + c Y = Y_k_1[1:] - Y_k_1[:-1] XY = np.concatenate(([Y_k_1[0]], Y)) print(XY) CA = np.abs(XY - X0) Theta = CA XD_Theta = CA / X0 AV = np.mean(CA) R_k = (np.min(Theta) + 0.5 * np.max(Theta)) / (Theta + 0.5 * np.max(Theta)) P = 0.5 R = np.sum(R_k) / len(R_k) print(R) Temp0 = (CA - AV) ** 2 Temp1 = np.sum(Temp0) / len(CA) S2 = np.sqrt(Temp1) AV_0 = np.mean(X0) Temp_0 = (X0 - AV_0) ** 2 Temp_1 = np.sum(Temp_0) / len(CA) S1 = np.sqrt(Temp_1) TempC = S2 / S1 * 100 C = str(TempC) + '%' SS = 0.675 * S1 Delta = np.abs(CA - AV) TempN = np.where(Delta <= SS)[0] N1 = len(TempN) N2 = len(CA) TempP = N1 / N2 * 100 P = str(TempP) + '%'

这段代码是用来计算指数平滑模型的预测值和评估模型拟合优度的指标。具体来说，根据输入的行数和列数创建一个矩阵，并根据用户输入的元素值进行填充。然后，根据指数平滑模型的公式计算预测值，并将其打印出来。接下来，根据预测值和原始值计算预测误差、平均绝对误差、相对误差等指标，并将结果打印出来。

请问您还有其他问题吗？

相关问题

下面的代码哪里有问题，帮我改一下from __future__ import print_function import numpy as np import tensorflow import keras from keras.models import Sequential from keras.layers import Dense,Dropout,Flatten from keras.layers import Conv2D,MaxPooling2D from keras import backend as K import tensorflow as tf import datetime import os np.random.seed(0) from sklearn.model_selection import train_test_split from PIL import Image import matplotlib.pyplot as plt from keras.datasets import mnist images = [] labels = [] (x_train,y_train),(x_test,y_test)=mnist.load_data() X = np.array(images) print (X.shape) y = np.array(list(map(int, labels))) print (y.shape) x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0) print (x_train.shape) print (x_test.shape) print (y_train.shape) print (y_test.shape) ############################ ########## batch_size = 20 num_classes = 4 learning_rate = 0.0001 epochs = 10 img_rows,img_cols = 32 , 32 if K.image_data_format() =='channels_first': x_train =x_train.reshape(x_train.shape[0],1,img_rows,img_cols) x_test = x_test.reshape(x_test.shape[0],1,img_rows,img_cols) input_shape = (1,img_rows,img_cols) else: x_train = x_train.reshape(x_train.shape[0],img_rows,img_cols,1) x_test = x_test.reshape(x_test.shape[0],img_rows,img_cols,1) input_shape =(img_rows,img_cols,1) x_train =x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:',x_train.shape) print(x_train.shape[0],'train samples') print(x_test.shape[0],'test samples')

代码中存在以下问题：

1. 变量 images 和 labels 没有被赋值，而后面却使用了它们来构造 X 和 y，导致 X 和 y 的值都为空。

2. 导入的 mnist 数据集没有被使用，而是使用了未定义的 images 和 labels，需要将其替换为 mnist 数据集。

3. input_shape 的值应该是三元组 (img_rows, img_cols, 1)，而代码中缺少了最后一个 1。

4. 在 if K.image_data_format() =='channels_first': 分支中，x_train 和 x_test 被改变了形状，但 y_train 和 y_test 却没有被改变，需要将其同步修改。

下面是修改后的代码：

from __future__ import print_function
import numpy as np
import tensorflow
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import tensorflow as tf
import datetime
import os
np.random.seed(0)
from sklearn.model_selection import train_test_split
from PIL import Image
import matplotlib.pyplot as plt
from keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
print(x_train.shape)
print(y_train.shape)

############################
##########
batch_size = 20
num_classes = 10
learning_rate = 0.0001
epochs = 10

img_rows, img_cols = 32, 32

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (img_rows, img_cols, 1)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print('input_shape:', input_shape)

img=cv.imread("E:\python\project\gray.jpg") img_float32=np.float32(img) dft=cv.dft(img_float32,flags=cv.DFT_COMPLEX_OUTPUT) dft_shift=np.fft.fftshift(dft) rows,cols=img.shape crow,ccol=int(row/2),int(cols/2) mask=np.zeros((rows,cols,2),np.uint8) mask[crow-30:crow+30,ccol-30:ccol+30]=1 fshift=dft_shift*mask f_ishift=np.fft.ifftshift(fshift) img_back=cv.idft(f_ishift) img_back=cv.magnitude(img_back[:,:,0],img_back[:,:,1]) plt.subplot(121).plt.imshow(img,camp='gray') plt.title('input image'),plt.xticks([]),plt.yticks([]) plt.subplot(122),plt.imshow(img_back,camp=gray) plt.titele('result'),plt.xticks([]),plt.yticks([]) plt.show() cv.waitKey(0) #按下任意键后再关闭显示窗口 cv.destroyAllWindows() 修改代码，解决图像不显示问题

要解决图像不显示的问题，你可以尝试按照以下方式修改代码：

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt

img = cv.imread("E:\python\project\gray.jpg")
img_float32 = np.float32(img)
dft = cv.dft(img_float32, flags=cv.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)
rows, cols = img.shape[:2]
crow, ccol = int(rows/2), int(cols/2)
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow-30:crow+30, ccol-30:ccol+30] = 1
fshift = dft_shift * mask
f_ishift = np.fft.ifftshift(fshift)
img_back = cv.idft(f_ishift)
img_back = cv.magnitude(img_back[:,:,0], img_back[:,:,1])

plt.subplot(121), plt.imshow(img, cmap='gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(img_back, cmap='gray')
plt.title('Result'), plt.xticks([]), plt.yticks([])
plt.show()

cv.waitKey(0) # 按下任意键后再关闭显示窗口
cv.destroyAllWindows()

这样修改后，应该可以正确显示图像了。注意检查文件路径是否正确，并确保安装了必要的库（如cv2、numpy和matplotlib）。