比偶工作大事记

1.单个文件数据清洗

import re
f=open("/Users/mingai/Downloads/4l8hA-1155-38.69.pdb","r")
lines=f.readlines()
f = open('/Users/mingai/Downloads/4l8hA-1155-38.69.txt','w')
ans=[]
patt = '-?\d+\.\d+'
for raw in lines:
    list = []
    while(re.search(patt,raw)):
        c1 = re.search(patt, raw).group()
        list.append(c1)
        raw=raw.replace(c1,"",1)
    if list != []:
        ans.append(list[:3])
for i in ans:
    if i != []:
        f.write(str(i))
f.close()

 

2.时间戳

量化是十分重要的思想，数据多，时间长，什么叫多，什么单位级别的，什么叫慢，大概执行需要多久，所以与之相关，时间戳和容量的计算就是十分重要的，比如一个程序耗时十分长，是哪个步骤导致的时间长，最终总共多久 是我们需要明确的问题

在此先说时间戳的问题

import datetime

因为数据量特别大,跑数据存储的一遍超级慢，所以痛定思痛，要量化产出

import datetime
t1 = datetime.datetime.now()
for i in range(100):
    print(i)
t2 = datetime.datetime.now()
print(t2-t1)
#datetime.timedelta(0, 3, 758215)
print((t2-t1).seconds)
print(datetime.datetime.now())

3.多线程，开CUDA加速方法

4.很好的带图像显示的方法

# -*- coding:utf-8 -*-
# author:zhangwei

import torch
import matplotlib.pyplot as plt

x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)
y = x.pow(2) + 0.2*torch.rand(x.size())                 # noisy y data (tensor), shape=(100, 1)
input=[[[0.000,0.000,0.000],[1.458,0.000,0.000],[2.009,1.420,0.000],[1.368,2.345,-0.500],[1.988,-0.778,-1.196],[1.988,-0.777,-1.195],[-0.500,-0.433,-0.750],[3.200,1.587,0.564],[3.932,2.844,0.452],[4.835,2.847,-0.775],[5.871,2.183,-0.797],[4.758,3.097,1.714],[5.001,4.140,3.538],[3.610,0.824,1.084],[4.435,3.599,-1.795],[5.049,3.491,-3.113],[5.933,4.696,-3.409],[5.644,5.811,-2.978],[3.968,3.344,-4.191],[3.467,2.141,-5.012],[3.687,4.263,-1.654],[7.013,4.463,-4.149],[7.811,5.551,-4.701],[7.487,5.780,-6.172],[8.111,6.610,-6.833],[9.304,5.259,-4.532],[10.764,5.480,-3.682],[7.287,3.509,-4.333],[6.507,5.040,-6.679],[5.945,5.311,-7.997],[4.506,5.799,-7.894],[3.727,5.297,-7.084],[5.989,4.062,-8.897],[6.281,3.637,-9.172],[6.141,4.271,-6.137],[4.160,6.782,-8.718],[2.860,7.436,-8.627],[2.124,7.388,-9.959],[2.708,7.644,-11.012],[2.996,8.904,-8.180],[2.883,9.337,-7.656],[4.813,7.083,-9.427],[0.838,7.057,-9.908],[0.037,6.905,-11.116],[-0.633,8.218,-11.502],[-1.322,8.836,-10.691],[-1.044,5.821,-10.945],[-1.206,5.270,-10.838],[0.404,6.907,-9.008],[-0.426,8.639,-12.745],[-1.157,9.771,-13.302],[-1.286,9.652,-14.815],[-0.467,9.005,-15.469],[-0.463,11.086,-12.927],[-0.788,12.183,-11.896],[0.255,8.163,-13.319],[-2.318,10.281,-15.367],[-2.563,10.237,-16.804],[-1.526,11.053,-17.565],[-0.644,11.667,-16.965],[-2.563,10.237,-16.804],[-2.950,10.802,-14.776],[-1.637,11.055,-18.889],[-0.584,11.587,-19.745],[-0.943,12.974,-20.263],[-0.525,13.368,-21.352],[-0.288,10.654,-20.905],[0.366,9.864,-21.126],[-2.473,10.679,-19.313],[-1.721,13.710,-19.477],[-2.006,15.109,-19.775],[-1.009,16.033,-19.086],[-0.235,15.601,-18.232],[-3.433,15.498,-19.347],[-4.481,15.517,-19.648],[-2.125,13.291,-18.652],[-1.034,17.307,-19.463],[-0.000,18.248,-19.048],[-0.199,18.683,-17.602],[-1.142,18.251,-16.938],[0.000,18.248,-19.048],[-1.787,17.632,-20.051],[0.695,19.539,-17.119],[0.694,19.937,-15.716],[-0.594,20.662,-15.350],[-0.975,20.719,-14.180],[1.902,20.824,-15.410],[3.825,21.030,-14.553],[1.392,19.925,-17.739],[-1.262,21.216,-16.356],[-2.480,21.986,-16.135],[-3.720,21.119,-16.309],[-4.847,21.596,-16.176],[-2.538,23.183,-17.088],[-2.336,24.181,-17.107],[-0.918,21.099,-17.298],[-3.506,19.842,-16.607],[-4.606,18.905,-16.799],[-5.282,19.118,-18.148],[-6.455,18.790,-18.324],[-4.607,18.904,-16.799],[-2.556,19.512,-16.703],[-4.534,19.669,-19.098],[-5.060,19.929,-20.432],[-4.732,18.788,-21.387],[-5.014,18.866,-22.583],[-4.508,21.247,-20.978],[-4.814,23.293,-21.420],[-3.576,19.913,-18.891],[-4.135,17.729,-20.851],[-3.853,16.532,-21.634],[-4.989,15.523,-21.525],[-5.620,15.394,-20.476],[-2.543,15.886,-21.173],[-0.845,15.708,-21.533],[-3.870,17.754,-19.877],[-5.245,14.808,-22.616],[-6.343,13.851,-22.665],[-5.973,12.629,-23.496],[-5.372,12.752,-24.563],[-7.621,14.485,-23.245],[-8.047,14.885,-23.233],[-4.663,14.932,-23.433],[-6.335,11.451,-23.001],[-6.165,10.216,-23.758],[-7.431,9.370,-23.723],[-7.991,9.118,-22.656],[-4.975,9.419,-23.212],[-3.539,9.199,-23.726],[-6.738,11.409,-22.076],[-7.878,8.934,-24.895],[-9.067,8.096,-24.998],[-8.694,6.637,-25.230],[-8.027,6.305,-26.209],[-9.993,8.566,-26.136],[-10.597,8.868,-26.270],[-7.382,9.189,-25.737],[-9.129,5.769,-24.322],[-8.780,4.355,-24.385],[-10.002,3.499,-24.689],[-10.752,3.127,-23.786],[-8.131,3.911,-23.069],[-6.672,3.614,-22.670],[-9.717,6.098,-23.570],[-10.198,3.188,-25.966],[-11.212,2.222,-26.373],[-10.787,0.800,-26.028],[-9.620,0.545,-25.733],[-11.521,2.333,-27.854],[-12.202,2.834,-28.476],[-9.631,3.633,-26.673],[-11.742,-0.123,-26.067],[-11.458,-1.530,-25.816],[-10.240,-1.995,-26.604],[-9.429,-2.778,-26.108],[-12.741,-2.257,-26.252],[-13.286,-0.619,-26.705],[-10.117,-1.510,-27.835],[-8.966,-1.826,-28.672],[-7.663,-1.427,-27.990],[-6.697,-2.189,-27.983],[-9.079,-1.212,-30.059],[-9.557,-0.974,-32.481],[-10.840,-0.906,-28.200],[-7.644,-0.228,-27.418],[-6.464,0.268,-26.719],[-6.548,-0.018,-25.225],[-5.744,0.487,-24.441],[-6.464,0.268,-26.719],[-8.468,0.354,-27.468],[-7.526,-0.829,-24.837],[-7.720,-1.179,-23.435],[-7.193,-2.577,-23.138],[-7.628,-3.556,-23.744],[-9.205,-1.105,-23.034],[-9.663,-0.744,-22.667],[-8.150,-1.214,-25.531],[-6.255,-2.663,-22.201],[-5.649,-3.939,-21.839],[-6.709,-4.957,-21.435],[-7.513,-4.707,-20.538],[-4.630,-3.774,-20.727],[-3.597,-3.616,-20.630],[-5.953,-1.824,-21.728],[-6.703,-6.105,-22.104],[-7.701,-7.139,-21.858],[-7.690,-7.581,-20.400],[-8.704,-8.036,-19.871],[-7.304,-8.279,-22.811],[-5.945,-7.200,-23.228],[-6.536,-7.444,-19.755],[-6.364,-7.912,-18.385],[-6.555,-6.778,-17.386],[-7.423,-6.844,-16.516],[-4.974,-8.540,-18.173],[-4.565,-8.938,-18.299],[-5.759,-7.003,-20.225],[-5.739,-5.738,-17.517],[-5.712,-4.657,-16.539],[-6.654,-3.529,-16.938],[-6.991,-2.671,-16.122],[-4.305,-4.121,-16.342],[-3.485,-4.290,-15.709],[-5.121,-5.694,-18.315],[-7.076,-3.535,-18.197],[-7.955,-2.493,-18.716],[-7.255,-1.140,-18.731],[-7.821,-0.133,-18.305],[-7.955,-2.493,-18.716],[-6.782,-4.281,-18.811],[-6.022,-1.123,-19.224],[-5.223,0.097,-19.247],[-4.881,0.505,-20.674],[-5.178,-0.218,-21.625],[-3.919,-0.067,-18.444],[-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label=V(t.Tensor([38.69])).float()
# 画图
# plt.scatter(x.data.numpy(), y.data.numpy())
# plt.show()

import torch
import torch.nn.functional as F     # 激励函数都在这

class Net(torch.nn.Module):  # 继承 torch 的 Module
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()     # 继承 __init__ 功能
        # 定义每层用什么样的形式
        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # 隐藏层线性输出
        self.predict = torch.nn.Linear(n_hidden, n_output)   # 输出层线性输出

    def forward(self, x):   # 这同时也是 Module 中的 forward 功能
        # 正向传播输入值, 神经网络分析出输出值
        x = F.relu(self.hidden(x))      # 激励函数(隐藏层的线性值)
        x = self.predict(x)             # 输出值
        return x

net = Net(n_feature=1, n_hidden=10, n_output=1)

print(net)  # net 的结构

# optimizer 是训练的工具
optimizer = torch.optim.Adam(net.parameters(), lr=0.01)  # 传入 net 的所有参数, 学习率
loss_func = torch.nn.MSELoss()      # 预测值和真实值的误差计算公式 (均方差)

# for t in range(100):
#     prediction = net(x)     # 喂给 net 训练数据 x, 输出预测值
#
#     loss = loss_func(prediction, y)     # 计算两者的误差
#     print(loss)
#
#     optimizer.zero_grad()   # 清空上一步的残余更新参数值
#     loss.backward()         # 误差反向传播, 计算参数更新值
#     optimizer.step()        # 将参数更新值施加到 net 的 parameters 上

import matplotlib.pyplot as plt

plt.ion()   # 画图
plt.show()

for t in range(200):

    prediction = net(x)  # 喂给 net 训练数据 x, 输出预测值

    loss = loss_func(prediction, y)  # 计算两者的误差
    # print(loss)

    optimizer.zero_grad()  # 清空上一步的残余更新参数值
    loss.backward()  # 误差反向传播, 计算参数更新值
    optimizer.step()  # 将参数更新值施加到 net 的 parameters 上

    # 接着上面来
    if t % 5 == 0:
        # plot and show learning process
        plt.cla()
        plt.scatter(x.data.numpy(), y.data.numpy())
        plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
        plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color':  'red'})
        plt.pause(0.2)