Python37\Lib\site-packages\networkx\classes\graph.py 以下为draw_networkx_nodes方法源码: def draw_networkx_nodes( G, pos, nodelist=None, node_size=300, node_color="#1f78b4", node_shape="o", alpha=None, cmap=None, vmin=None, vmax=None, ax=None, linewidths=None, edgecolors=None, label...
L = LucidSonicDream(song = 'lucidsonicdreams_main.mp3', pulse_audio = 'lucidsonicdreams_pulse.mp3', class_audio = 'lucidsonicdreams_class.mp3', style = 'wikiart') L.hallucinate('lucidsonicdreams.mp4', pulse_react = 0.25, motion_react = 0, classes = [1,5,9,16,23,27,28,30,50,...
@SVMClassdef multi_fit(self, X, y, eval_train=False):self.k = len(np.unique(y)) # number of classes# for each pair of classesfor i in range(self.k):# get the data for the pairXs, Ys = X, copy.copy(y)# change the labels to -1 and...
iris=datasets.load_iris()X=iris.data #X.shape==(150,4)y=iris.target # y.shape==(150,)# 二进制化输出 y=label_binarize(y,classes=[0,1,2])# shape==(150,3)n_classes=y.shape[1]# n_classes==3#np.r_是按列连接两个矩阵,就是把两矩阵上下相加,要求列数相等。 #np.c_是按行连接...
Many chapters in this tutorial end with an exercise where you can check your level of knowledge. See all Python Exercises Python Examples Learn by examples! This tutorial supplements all explanations with clarifying examples. Python Quiz Test your Python skills with a quiz. ...
for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and accurate evaluation of a large variety of ...
("classes: ",oe.categories_)# 调用transform获得编码结果# transform默认返回稀疏矩阵,当分类变量包含很多类别时非常有用,进一步调用toarray可获得熟悉的numpy二维数组# 创建OneHotEncoder实例时,如果设置sparse=False,调用transform会得到二维数组encoded_labels=oe.transform(car_types.reshape(-1,1)).toarray()print...
接下来介绍一些类的特征：the class inheritance mechanism allows multiple base classes, a derived class...
detection. output_dict= run_inference_for_single_image(image_np_expanded, detection_graph) # Visualization of the results of a detection. vis_util.visualize_boxes_and_labels_on_image_array( image_np, output_dict['detection_boxes'], output_dict['detection_classes'], output...
self.fc2 = Linear(in_features=64, out_features=num_classes) # 网络的前向计算过程 def forward(self, x): x = self.conv1(x) # 每个卷积层使用Sigmoid激活函数,后面跟着一个2x2的池化 x = F.sigmoid(x) x = self.max_pool1(x) x = F.sigmoid(x...