首页for N in range(5, Nmax2+1): X_train = np.array(range(len(df_temp['adj_close'][-N-1:-1]))) y_train = np.array(df_temp['adj_close'][-N-1:-1]) X_train = X_train.reshape(-1, 1) y_train = y_train.reshape(-1, 1) regr.fit(X_train, y_train) y_est = regr.predict(X_train)

for N in range(5, Nmax2+1): X_train = np.array(range(len(df_temp['adj_close'][-N-1:-1]))) y_train = np.array(df_temp['adj_close'][-N-1:-1]) X_train = X_train.reshape(-1, 1) y_train = y_train.reshape(-1, 1) regr.fit(X_train, y_train) y_est = regr.predict(X_train)

时间: 2024-01-26 22:02:00 浏览: 54

colourhistogram.rar_CBIR.fig_DWT.fig_cbir_cbir dwt_feature band

Colourhistogram II. TEXTURE FEATURE EXTRACTION IN CBIR An overview of the proposed CBIR system is illustrated in Fig. 1. The proposed algorithm, Label Wavelet Transform (LWT), is based on color image segmentation [1], and it is an extension of DWT-based texture feature extraction method. The 2-D DWT is computed by applying separable filter banks to the gray level images. The detail images Dn,1, Dn,2, and Dn,3 are obtained by band-pass filtering in a specific direction, and they can be categorized into three frequency bands: HL, LH, HH band, respectively. Each band contains different directional information at scale n. The texture feature is extracted from the variance (ó2 n,i) of the coefficients cn,i of the detail image Dn,1, Dn,2, and Dn,3 at different scale n.To represent the texture feature of an image q, the texture feature vector of DWT is defined as [2]: TDWT (q) = [ó2 1,1, ó2 1,2, ó2 1,3, ..., ó2N max,3], (1) where Nmax denotes the largest scale. In this work, Nmax

这段代码是什么意思？这段代码是一个循环，从5开始循环到Nmax2+1为止。在每次循环中，它会构建一个X_train和y_train的训练数据集，其中X_train是一个包含从0到N-1的数字的数组，y_train是一个包含df_temp['adj_close']最后N个数据的数组。然后，将X_train和y_train传递给线性回归模型regr进行拟合，得到y_est的预测结果。

阅读全文