fig = plt.figure(figsize=frame_param.long_fig[0], dpi=frame_param.long_fig[1]) geo_axes, proj_1 = axes_helper.get_geo_axes(fig, frame_param.long_fig_geosize) show_fig_logo(fig) colorbar_axes = fig.add_axes(frame_param.colorbar_axes) ct = ColorTable() rc12 = ct.ColorRecords['ARI'] # 填色 v = sta_ari1['ARI'] x, y = sta_ari1['lon'], sta_ari1['lat'] cs = geo_axes.scatter(x, y, s=100, c=sta_ari1['ARI'], cmap='Blues', linewidths=1, edgecolor='black', alpha=0.75) # 添加颜色条 # 填图 meb.set_customized_shpfile_list([r"F:\maskout\安徽"]) # shp掩膜 #colorbar_axes.text(1,rc12.Level[0],'单位：'+rc12.Unit,fontsize=16,fontproperties = song_fontprop,color='Black') cb = plt.colorbar(cs, cax=colorbar_axes, orientation='vertical') cb.set_ticklabels(rc12.RetStrLevel()) geo_axes.spines['geo'].set_visible(False) # plt.imshow(img_logo) # geo_axes.imshow(img_logo) # 制作表格 subdir_time = datetime.datetime.strptime(subdir, '%Y%m%d%H') b_time = subdir_time + timedelta(hours=leadtime) table_axes = fig.add_axes(frame_param.long_fig_tablesize) plt.sca(table_axes) table_axes.text(-1.3, 0.93, '安徽省降水历史重现期图', color='r', fontsize=35, fontproperties=hei_fontpropeti) table_axes.text(-0.8, 0.85, '起:' + subdir_time.strftime('%m月%d日%H时\n') + '止:' + b_time.strftime('%m月%d日%H时'), color='k', fontsize=20, fontproperties=hei_fontpropeti) axes_helper.axes_edge_dele(table_axes)我想把fig比例更改但是不影响table_axes在fig中的位置

colourhistogram.rar_CBIR.fig_DWT.fig_cbir_cbir dwt_feature band

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 ...

ice.fig.rar_ ice.fig_ice.fig_ice函数_matlab ice_matlab ice函数

matlab ice.fig 函数源代码

fig = plt.figure(figsize=frame_param.long_fig[0], dpi=frame_param.long_fig[1]) geo_axes, proj_1 = axes_helper.get_geo_axes(fig, frame_param.long_fig_geosize) show_fig_logo(fig) colorbar_axes = fig.add_axes(frame_param.colorbar_axes) ct = ColorTable() rc12 = ct.ColorRecords['Rain12HCR'] x, y, v = get_grid_data(rain_frame, 'sum') # 填色 v = gaussian_filter(v, sigma=3) cs = geo_axes.contourf(x, y, v, levels=rc12.Level, colors=rc12.StrColor) # 填图 c_level_station = rain_frame_ah[ (rain_frame_ah['sw_flag'] == 0) & (rain_frame_ah['Station_Id_C'].str.startswith('5'))] c_level_station = c_level_station.reset_index()可以帮我改成在地里坐标轴上绘制散点图吗

fig = plt.figure(figsize=frame_param.long_fig[0], dpi=frame_param.long_fig[1]) geo_axes, proj_1 = axes_helper.get_geo_axes(fig, frame_param.long_fig_geosize) show_fig_logo(fig) colorbar_axes = fig.add...

现有1500个二维空间的数据点，数据产生代码范例如下所示 import time as time import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d.axes3d as p3 from sklearn.datasets import make_swiss_roll # Generate data (swiss roll dataset) n_samples = 1500 noise = 0.05 X, _ = make_swiss_roll(n_samples, noise=noise) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(X[:, 0], X[:, 1], X[:, 2], cmap=plt.cm.Spectral) ，编写一个python程序不调用locally_linear_embedding，实现LLE降维

plt.scatter(X_lle[:, 0], X_lle[:, 1], cmap=plt.cm.Spectral) plt.show() 在上述代码中，我们首先计算每个样本的k个邻居，并计算每个样本的权重矩阵W。然后，我们计算重构权重矩阵M，并计算特征值和特征向量...

def Draw_Classification_Map(label, name: str, scale: float = 4.0, dpi: int = 400): ''' get classification map , then save to given path :param label: classification label, 2D :param name: saving path and file's name :param scale: scale of image. If equals to 1, then saving-size is just the label-size :param dpi: default is OK :return: null ''' fig, ax = plt.subplots() numlabel = np.array(label) v = spy.imshow(classes=numlabel.astype(np.int16), fignum=fig.number) ax.set_axis_off() ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) fig.set_size_inches(label.shape[1] * scale / dpi, label.shape[0] * scale / dpi) foo_fig = plt.gcf() # 'get current figure' plt.gca().xaxis.set_major_locator(plt.NullLocator()) plt.gca().yaxis.set_major_locator(plt.NullLocator()) plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0) foo_fig.savefig(name + '.png', format='png', transparent=True, dpi=dpi, pad_inches=0) pass怎么才能把这段代码输出的超像素级图片转换为像素级图片

pixel_value = superpixel_image[center[0], center[1]] image[markers == label] = pixel_value # 保存像素级别的图像 cv2.imwrite('pixel_image.png', image) 请注意，以上代码示例仅供参考，具体实现可能...

import pandas as pd import warnings import sklearn.datasets import sklearn.linear_model import matplotlib import matplotlib.font_manager as fm import matplotlib.pyplot as plt import numpy as np import seaborn as sns data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') print(data.info()) fig = plt.figure(figsize=(10, 8)) sns.heatmap(data.corr(), cmap="YlGnBu", annot=True) plt.title('相关性分析热力图') plt.rcParams['axes.unicode_minus'] = False plt.rcParams['font.sans-serif'] = 'SimHei' plt.show() y = data['y'] X = data.drop(['y'], axis=1) print('输出新的特征集数据***') print(X.head()) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) def relu(X): output=np.maximum(0, X) return output def relu_back_propagation(derror_wrt_output,X): derror_wrt_dinputs = np.array(derror_wrt_output, copy=True) derror_wrt_dinputs[x <= 0] = 0 return derror_wrt_dinputs def activated(activation_choose,X): if activation_choose == 'relu': return relu(X) def activated_back_propagation(activation_choose, derror_wrt_output, output): if activation_choose == 'relu': return relu_back_propagation(derror_wrt_output, output) class NeuralNetwork: def init(self, layers_strcuture, print_cost = False): self.layers_strcuture = layers_strcuture self.layers_num = len(layers_strcuture) self.param_layers_num = self.layers_num - 1 self.learning_rate = 0.0618 self.num_iterations = 2000 self.x = None self.y = None self.w = dict() self.b = dict() self.costs = [] self.print_cost = print_cost self.init_w_and_b()

其中，relu() 函数实现了 ReLU 激活函数，即将输入 X 中小于 0 的值置为 0，大于等于 0 的值保持不变；relu_back_propagation() 函数则实现了 ReLU 函数的反向传播过程，即计算误差关于输入的偏导数。接着，定义了...

帮我调整一下SVM的超参数，提高测试集得分：from sklearn.neighbors import KNeighborsClassifier from sklearn.datasets import load_digits from sklearn import svm, metrics from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix from sklearn.metrics import plot_confusion_matrix import numpy as np #使绘图支持中文字符 from matplotlib import rcParams rcParams['font.family'] = 'SimHei' plt.rcParams['axes.unicode_minus'] = False In[2]: digits = load_digits() data = digits.data print(data[0]) print(digits.images[0]) print(digits.target[0]) plt.imshow(digits.images[0]) plt.show() In[3]: train_x, test_x, train_y, test_y = train_test_split(data, digits.target, test_size=0.3, random_state=82) print(train_x) clf = svm.SVC(kernel='linear') clf.fit(train_x, train_y) print("svm训练集得分: %.4lf" % clf.score(train_x, train_y)) print("svm测试集得分: %.4lf" % clf.score(test_x, test_y)) print(clf.predict(data)) plot_confusion_matrix(clf, test_x, test_y) plt.show() In[5]: ################################################################# fig = plt.figure(figsize=(6,13)) for i in range(40): y_pred = clf.predict([data[i]]) plt.subplot(8,5,i+1) plt.imshow(digits.images[i], interpolation='none') plt.title("%d---->%d"%(digits.target[i],y_pred)) plt.show()

fig, axes = plt.subplots(8, 5, figsize=(6, 13)) for i, ax in enumerate(axes.flat): y_pred = clf_best.predict([data[i]]) ax.imshow(digits.images[i], interpolation='none') ax.set_title("%d---->%d" % ...

from sklearn.neighbors import KNeighborsClassifier from sklearn.datasets import load_digits from sklearn import svm, metrics from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix from sklearn.metrics import plot_confusion_matrix import numpy as np #使绘图支持中文字符 from matplotlib import rcParams rcParams['font.family'] = 'SimHei' plt.rcParams['axes.unicode_minus'] = False # In[2]: digits = load_digits() data = digits.data print(data[0]) print(digits.images[0]) print(digits.target[0]) plt.imshow(digits.images[0]) plt.show() train_x, test_x, train_y, test_y = train_test_split(data, digits.target, test_size=0.3, random_state=93) print(train_x) knn = KNeighborsClassifier(n_neighbors=5) knn.fit(train_x, train_y) print("KNN训练集得分: %.4lf" % knn.score(train_x, train_y)) print("KNN测试集得分: %.4lf" % knn.score(test_x, test_y)) print(knn.predict(data)) plot_confusion_matrix(knn, test_x, test_y) plt.show() # In[5]: ################################################################# fig = plt.figure(figsize=(6,13)) for i in range(40): y_pred = knn.predict([data[i]]) plt.subplot(8,5,i+1) plt.imshow(digits.images[i], interpolation='none') plt.title("%d---->%d"%(digits.target[i],y_pred)) plt.show() 帮我修改一下代码，让测试集结果更准确

标准化可以使特征的均值为0，方差为1，有助于提高模型的性能。 python from sklearn.preprocessing import StandardScaler scaler = StandardScaler() train_x_scaled = scaler.fit_transform(train_x) test_x_...

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np # 生成随机三维数据点 np.random.seed(0) points = np.random.rand(8, 3) # 计算Convex Hull hull = ConvexHull(points) # 可视化Convex Hull fig = plt.figure() ax = fig.add_subplot(111, projection="3d") for s in hull.simplices: ax.plot(points[s, 0], points[s, 2], points[s, 1], "b-") plt.show() from scipy.spatial import distance def douglas_peucker(points, tolerance): """ 使用Douglas-Peucker算法将多边形折线进行简化 :param points: 多边形点集，numpy数组，每行表示一个三维点 :param tolerance: 简化误差容忍度 :return: 简化后的多边形点集 """ if len(points) < 3: return points # 找到距离最远的点作为分割点 dmax = 0 index = 0 end = len(points) - 1 for i in range(1, end): d = distance.point_line_distance(points[i], points[0], points[end]) if d > dmax: index = i dmax = d # 如果距离最远点小于容忍度，则直接返回两个端点 if dmax < tolerance: return np.array([points[0], points[end]]) # 递归地对两个子区间进行简化 left_points = douglas_peucker(points[:index+1], tolerance) right_points = douglas_peucker(points[index:], tolerance) # 返回合并后的结果 return np.vstack((left_points[:-1], right_points)) 请将这两个代码段合并，使其能够接连工作。

ax = fig.add_subplot(111, projection="3d") for s in hull.simplices: ax.plot(simplified_points[s, 0], simplified_points[s, 2], simplified_points[s, 1], "b-") plt.show() 这段代码的运行结果是生成一...

from scipy.stats import randint random_params = {'n_estimators': randint(low=80, high=200), 'max_features': randint(low=2, high=8)} forset_reg = RandomForestRegressor(random_state=2020) random_search_forest = RandomizedSearchCV(forest_reg, param_distributions=random_params, n_iter=20, scoring='neg_mean_squared_error', cv=5, random_state=2020) random_search_forest.fit(housing_prepares, housing_label) cvrus = random_search_forest.cv_results_ for mean_score, params in zip(cvrus['mean_test_score'], cvrus['params']): print(np.sqrt(-mean_score), params) print('随机搜索下最佳参数', random_search_forest.best_params_) print('随机搜索下最佳评估器', random_search_forest.best_estimator_) 将以上代码用plot绘图

fig, ax = plt.subplots(figsize=(12, 8)) for n_estimators in range(80, 201, 20): df = cvres_df[cvres_df['param_n_estimators'] == n_estimators] ax.plot(df['param_max_features'], np.sqrt(-df['mean_...

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV # 这里对随机森林模型进行参数搜索 param_grid = [ {'n_estimators': [80, 100, 120], 'max_features': [2, 6, 10]}, {'bootstrap': [False], 'n_estimators': [80, 100, 120], 'max_features': [2, 6, 10]} ] forest_reg = RandomForestRegressor() grid_search_forest = GridSearchCV(forest_reg, param_grid=param_grid, scoring='neg_mean_squared_error', cv=5) grid_search_forest.fit(housing_prepares, housing_label) print('网格搜索下最佳参数', grid_search_forest.best_params_) print('网格搜索下最佳评估器', grid_search_forest.best_estimator_) # 不同超参数下的模型得分 cvres = grid_search_forest.cv_results_ for mean_score, params in zip(cvres['mean_test_score'], cvres['params']): print(np.sqrt(-mean_score), params) 将以上代码用plot绘图

fig, ax = plt.subplots(figsize=(12, 8)) for n_estimators in [80, 100, 120]: df = cvres_df[cvres_df['param_n_estimators'] == n_estimators] ax.plot(df['param_max_features'], np.sqrt(-df['mean_test_...

utils.vis_utils 保存图片

fig.savefig(file_path) 其中，fig 是 matplotlib 中的 figure 对象，file_path 是图片保存路径，可以是相对路径或绝对路径，包含文件名和格式，例如：'./test.png'。调用该方法时，可以将 matplotlib ...

根据输入数据绘制不同的两个子图 :param x: 输入数据,类型为array :return: None

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) # 创建一个1行2列的网格 ax1.plot(x[:, 0], label='Subplot 1') # 绘制第一个子图的数据 ax1.set_xlabel('X-axis') ax1.set_ylabel('Y-axis 1') ax1....

1)根据语音分帧的思想，编写分帧函数。函数定义如下:函数格式: frameout = enframe(x, win,inc ) 输人参数: x是语音信号; win 是帧长或窗函数，若为窗函数，帧长便取窗函数长; ine 是帧移。输出参数: frameout 是分帧后的数组，长度为帧长和帧数的乘积。根据分帧后的语音，绘制连续四帧语音信号(不用窗函数)，效果如图3-3所示。

fig, ax = plt.subplots(nrows=4, sharex=True, sharey=True) for i in range(4): ax[i].plot(frames[i]) plt.show() 这段代码会将分帧后的前四帧语音信号绘制在同一个图中，效果类似于图3-3所示。如果要绘制...

使用python创建一个使用归一化、Xavier初始化、正则化方法和MSGD的BP网络，该网络有8个输入1个输出1层隐藏层，要求分为两个模块（py文件），在确认输入、输出的训练集之后，模型不会出现shape类报错，且一个是BP网络，另一个是主函数，在每句代码后添加注释；训练集数据存于xlsx表格前3000行中，测试集为之后的40行，且均为float类型。并且能够输出各输入的权重。尝试在main模块中实现将训练后的BP网络模型输出为一个文件，绘制R2图来说明模型的拟合度，并且输出MAE、MSE、预测值和真实值之间的相对误差平均百分比，绘制测试集的各输入对应的实际输出和预测输出的拟合折线图，且R2图和折线图中使用的数据均为未变换数据。

fig, ax = plt.subplots(figsize=(10, 6)) for i in range(X_test.shape[1]): ax.plot(X_test[:, i], y_test, 'o', label='Actual') ax.plot(X_test[:, i], y_pred, 'x', label='Predicted') ax.set_xlabel('...

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