import matplotlib.pyplot as plt. import numpy as np. def generate_exponential_data(): """ 生成指数数据。这里我们假设数据代表时间序列上的某种增长现象, 时间范围从0到10,共100个时间点。 return: 包含时间和对应增长值的元组。 """ time_values = np.lins
where the violin plot indicates the median (middle line), maximum (upper line) and minimum (lower line) hazard factor values, “ws” refers to the intensity of wind shear, and “turb” stands for the turbulence intensity. The higher values of both “ws” and “turb” indicate stronger int...
• Plots and figures using the matplotlib libraries. • Graph analysis using the python networkx libraries. E.8 R R is a contemporary statistical analysis package. The R Project. (www.r-project.org) E.9 C/C++ Status Current work is increasing use of C++ container classes and iterators ...
The project requiresscikit-learn,matplotlibandNLoptto run. Usage example: # load `Lung cancer' dataset from mldata.orgcancer=fetch_mldata("Lung cancer (Ontario)")X=cancer.target.Tytrue=np.copy(cancer.data).flatten()ytrue[ytrue>0]=1# label a few pointslabeled_N=4ys=np.array([-1]*len...
vertex points to form a polygon shape. For doppler and m-mode echocardiograms, masks were drawn around the general shape of the waveforms and field of view. The labeling tool is an interactive polygon editor built with matplotlib that sets pixels inside the mask to 1 and pixels outside to ...
import dgl import numpy as np import networkx as nx import torch import torch.nn as nn import torch.nn.functional as F from dgl.nn.pytorch import GraphConv import itertools import matplotlib.pyplot as plt def build_karate_club_graph(): src = np.array([1, 2, 2, 3, 3, 3, 4, 5, ...
where the violin plot indicates the median (middle line), maximum (upper line) and minimum (lower line) hazard factor values, “ws” refers to the intensity of wind shear, and “turb” stands for the turbulence intensity. The higher values of both “ws” and “turb” indicate stronger int...
frommatplotlibimportpyplotasplt # 超参数设置 x_height,x_width=[28,28] num_channels=1 num_classes=10 latent_size=100 labeled_rate=0.1 # 设置训练参数保存和模型保存的路径 log_path='./SS_GAN_log.csv' model_path='./SS_GAN_model.ckpt' ...
from matplotlib import pyplot from keras import backend # custom activation function def custom_activation(output): logexpsum = backend.sum(backend.exp(output), axis=-1, keepdims=True) result = logexpsum / (logexpsum + 1.0) return result # define the standalone supervised and unsupervised dis...
import matplotlib.pyplot as plt, numpy as np from sklearn.decomposition import PCA from sklearn import linear_model from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import Perceptron from sklearn.linear_model import SGDCla...