12345# Tensor常见形式# 0:scalar# 1:vector# 2:matrix# 3:n-dimensional tensor 12import torchfrom torch import tensor 123# scalarx = tensor(42.)x tensor(42.) 1x.dim() 0 1x.item() 42.0 123#

1import torch 123# 求导方法1x = torch.randn(3,4, requires_grad=True)x tensor([[ 2.2854, 0.9512, -2.4467, -0.7123], [-0.7575, 1.1755, 0.4198, -0.2310], [-0.9698, 1.2129, 1.3573,

12import torchtorch.__version__ '1.5.1' 123# 空向量x = torch.empty(5,3)x tensor([[ 0.0000e+00, 0.0000e+00, 0.0000e+00], [ 0.0000e+00, 0.0000e+00, 1.1614e-41], [ 0.000

12345678910import reimport pandas as pdimport matplotlib.pylab as pltimport seaborn as snsfrom fbprophet import Prophetplt.style.use('ggplot')plt.rcParams['font.sans-serif'] = [&#x

12345import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport re%matplotlib inline 12train = pd.read_csv('./data/train_1.csv').fillna(0)train.head() 1train.info() <

123456789import matplotlib.pylab as pltimport seaborn as snsfrom sklearn.metrics import classification_reportfrom sklearn.model_selection import train_test_splitfrom sklearn.tree import DecisionTreeCl

1234567891011import pandas as pd# import pandas_datareaderimport datetimeimport matplotlib.pylab as pltimport seaborn as snsfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacffrom statsmodel

12345678910111213141516171819202122import sysimport osimport pandas as pdimport numpy as npimport statsmodels.api as smimport statsmodels.formula.api as smfimport statsmodels.tsa.api as smtimport matp

12import numpy as npimport pandas as pd 时间序列 时间戳 时间间隔 固定周期 12345# D 天# H 小时# M 月rng = pd.date_range('2016/07/01', periods=10 , freq='D')rng DatetimeIndex(['2016-07-01'

康威定律指出系统的架构往往受制于产生这些设计的组织的沟通结构,高效的智能合约微服务化团队往往会促进智能合约微服务化开发效率,如图3所示给出了智能合约微服务化团队的范例,对于智能合约微服务化团队有如下建议: 两个披萨原则,如果两个披萨不足以喂饱整个项目团队,那么项目团队就太大了,即采用小而精的团队.根据沟通渠道公式n(n-1)/2(n代表组织中的人数),有效的沟通和反馈是团队高效运作的核心