label_means = np.arange(n_labels)[:,None] * np.ones((n_labels, n_times)) label_maxs = np.arange(n_labels)[:,None] * np.ones((n_labels, n_times))# compute the mean with sign fliplabel_means_flipped = np.zeros_like(label_means)fori, labelinenumerate(labels): label_means_fli...
n=5 x = np.arange(n) y = np.sin(np.linspace(-3,3,n)) xlabels = ['Ticklabel %i' % i for i in range(n)] fig, axs = plt.subplots(1,3, figsize=(12,3)) ha = ['right', 'center', 'left'] for n, ax in enumerate(axs): ax.plot(x,y, 'o-') ax.set_title(ha[n...
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index 限制图片尺寸 self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride 1.2 __getitem__ 1.2.1 将图片尺寸转换为batch shape letterbox: 作用:将图像转换为目标尺寸,new_shape...
object_indexes = np.arange(nobjects, dtype=np.int32)+1## First, compute the first and second nearest neighbors,# and the angles between self and the first and second# nearest neighbors#ocenters =centers_of_labels( objects.small_removed_segmented).transpose() ncenters =centers_of_labels...
1、scatter函数原型 2、其中散点的形状参数marker如下: 3、其中颜色参数c如下:4、基本的使用方法如下:[python] view plain copy #导入必要的模块 import numpy as np import matplotlib.pyplot as plt #产生测试数据 x = np.arange(1,10) y = x
importmatplotlib.pyplotaspltimportnumpyasnp# 创建数据x=np.arange(5)y=x**2# 创建图表fig,ax=plt.subplots()ax.plot(x,y)# 设置主要刻度标签ax.xaxis.set_ticklabels(['A','B','C','D','E'],fontdict={'fontsize':14,'color':'red'})# 设置次要刻度标签ax.xaxis.set_minor...
1、scatter函数原型 2、其中散点的形状参数marker如下: 3、其中颜色参数c如下:4、基本的使用方法如下:[python] view plain copy #导入必要的模块 import numpy as np import matplotlib.pyplot as plt #产生测试数据 x = np.arange(1,10) y = x
n=100X= np.random.uniform(low=-4.0, high=4.0, size=(n,1))f= lambda x: -2.5* x +3.2y= f(X) + np.random.normal(scale=0.15* np.max(y), size=(n,1)) copy We can visualize this data set in the figure below. Figure 7.1. Data for a synthetic regression problem to which we ...
assertnum_classes >= np.max(vector) result = np.zeros(shape=(len(vector), num_classes)) result[np.arange(len(vector)), vector] =1 returnresult.astype(np.float32) defmake_labels(filenames): n = len(filenames) #y = np.zeros((n,2), dtype = np.int32) ...
/usr/bin/python3 import matplotlib import matplotlib.pyplot as plt import numpy as np fig = matplotlib.pyplot.figure(figsize=(7,4), dpi=96, facecolor='#eeeeee', tight_layout=1) ax = fig.add_subplot(111) x = np.arange(0., 10., 0.1) ax.plot(x, 12+x**1.5+0.1*x**2.5) ## ...