我想仅使用带有svg/canvas和js最大值的css来逐行绘制线条.可以在这里找到我想绘制的线条的想法
<svg width="640" height="480" xmlns="http://www.w3.org/2000/svg">
<!-- Created with SVG-edit - http://svg-edit.googlecode.com/ -->
<g>
<title>Layer 1</title>
<path d="m33,104c1,0 2.1306,-0.8037 23,3c9.07012,1.65314 10,2 24,2c7,0 29,0 33,0c8,0 9,0 11,0c2,0 8,0 11,0c9,0 17,0 18,0c10,0 12,0 20,0c1,0 6,0 7,0c2,0 3.07613,0.38268 4,0c2.61313,-1.08239 2,-3 2,-6c0,-1 0,-2 0,-3c0,-1 0,-2 0,-3c0,-1 0,-2 0,-3c0,-1 0.30745,-3.186 -1,-5c-0.8269,-1.14727 -0.09789,-2.82443 -2,-4c-0.85065,-0.52573 -2.82443,-0.09789 -4,-2c-0.52573,-0.85065 -2.58578,-0.58578 -4,-2c-0.70711,-0.70711 -1.81265,-1.20681 -4,-3c-2.78833,-2.28588 -3.64749,-2.97251 -8,-4c-2.91975,-0.68926 -4.82375,-2.48626 -7,-3c-2.91975,-0.68926 -5.15224,-0.23463 -7,-1c-1.30656,-0.5412 -4.38687,-1.91761 -7,-3c-1.84776,-0.76537 -5.03609,0.37821 -7,0c-5.28799,-1.01837 -8,-3 -9,-3c-2,0 -5.0535,-0.54049 -7,-1c-2.17625,-0.51374 -4.15224,-0.23463 -6,-1c-1.30656,-0.54119 -3,-1 -4,-1c-2,0 -5,-1 -6,-1c-1,0 -3,-2 -6,-2c-2,0 -5,-2 -6,-2c-2,0 -2.02583,-0.67963 …我正在寻找初始化非空对象的数组/列表 - 类构造函数生成数据.在C++和Java中,我会做这样的事情:
Object lst = new Object[100];
我已经挖过了,但是有没有Pythonic方法来完成这项工作？
这不像我想的那样工作(我得到100个引用同一个对象):
lst = [Object()]*100
但这似乎按我想要的方式工作:
lst = [Object() for i in range(100)]
对于在Java中如此简单的事情,列表理解似乎(智力上)就像"很多"工作一样.
我正在使用Tkinter FigureCanvasTkagg绘制一些数据matplotlib.我需要清除图中我绘制数据的位置,并在按下按钮时绘制新数据.
这是代码的绘图部分(之前定义了一个App类):
self.fig = figure()
self.ax = self.fig.add_subplot(111)
self.ax.set_ylim( min(y), max(y) )
self.line, = self.ax.semilogx(x, y, '.-') #tuple of a single element
self.canvas = FigureCanvasTkAgg(self.fig, master=master)
self.ax.semilogx(x, y, 'o-')
self.canvas.show()
self.canvas.get_tk_widget().pack(side='top', fill='both', expand=1)
self.frame.pack()
如何更新此类画布的内容？
我不断从以下代码中获取input_shape错误.
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
def _load_data(data):
"""
data should be pd.DataFrame()
"""
n_prev = 10
docX, docY = [], []
for i in range(len(data)-n_prev):
docX.append(data.iloc[i:i+n_prev].as_matrix())
docY.append(data.iloc[i+n_prev].as_matrix())
if not docX:
pass
else:
alsX = np.array(docX)
alsY = np.array(docY)
return alsX, alsY
X, y = _load_data(dframe)
poi = int(len(X) * .8)
X_train = X[:poi]
X_test = X[poi:]
y_train = y[:poi]
y_test = y[poi:]
input_dim = 3
以上所有都顺利进行.这是它出错的地方.
in_out_neurons = 2
hidden_neurons …我正在通过使用随机梯度下降的反向传播训练XOR神经网络.将神经网络的权重初始化为-0.5和0.5之间的随机值.神经网络在80%的时间内成功训练自己.然而有时它会在反向传播时"卡住".通过"卡住",我的意思是我开始看到纠错率下降.例如,在成功培训期间,总误差会随着网络的学习而迅速下降,如下所示:
...
...
Total error for this training set: 0.0010008071327708653
Total error for this training set: 0.001000750550254843
Total error for this training set: 0.001000693973929822
Total error for this training set: 0.0010006374037948094
Total error for this training set: 0.0010005808398488103
Total error for this training set: 0.0010005242820908169
Total error for this training set: 0.0010004677305198344
Total error for this training set: 0.0010004111851348654
Total error for this training set: 0.0010003546459349181
Total error for this training set: 0.0010002981129189812
Total error for this training set: 0.0010002415860860656 …我有一个2D numpy数组,我想用3D绘制它.我听说过mplot3d,但我无法正常工作
这是我想要做的一个例子.我有一个尺寸为(256,1024)的数组.它应该绘制一个3D图形,其中x轴从0到256 y轴从0到1024,图形的z轴显示每个条目的数组值.
我该怎么做？
如何将3D散点图与3D曲面图结合起来,同时保持曲面图透明,这样我仍然可以看到所有点？
我有这个函数来计算向量x的平方Mahalanobis距离意味着:
def mahalanobis_sqdist(x, mean, Sigma):
'''
Calculates squared Mahalanobis Distance of vector x
to distibutions' mean
'''
Sigma_inv = np.linalg.inv(Sigma)
xdiff = x - mean
sqmdist = np.dot(np.dot(xdiff, Sigma_inv), xdiff)
return sqmdist
我有一个形状为的numpy数组(25, 4).所以,我想在没有for循环的情况下将该函数应用于我的数组的所有25行.那么,基本上,我该如何编写这个循环的矢量化形式:
for r in d1:
mahalanobis_sqdist(r[0:4], mean1, Sig1)
在哪里mean1和Sig1是:
>>> mean1
array([ 5.028, 3.48 , 1.46 , 0.248])
>>> Sig1 = np.cov(d1[0:25, 0:4].T)
>>> Sig1
array([[ 0.16043333, 0.11808333, 0.02408333, 0.01943333],
[ 0.11808333, 0.13583333, 0.00625 , 0.02225 ],
[ 0.02408333, 0.00625 , …我想知道是否有人能指出我用于比较时间相关信号的算法/技术.理想情况下,这个假设算法将接收2个信号作为输入并返回一个数字,该数字将是信号之间的百分比相似性(0表示2个信号在统计上不相关,1表示它们是完美匹配).
当然,我意识到我的请求存在问题,即我不确定如何在比较这两个信号的情况下正确定义"相似性",所以如果有人也能指出我正确的方向(至于我应该查看/知道等等,我也很感激.
我开始使用pyGTK +了解使用python和matplotlib进行交互式绘图.因此,我看了一下matplotlib网站上给出的例子.
这是本守则的简短内容:
#!/usr/bin/env python
"""
Example of embedding matplotlib in an application and interacting with
a treeview to store data. Double click on an entry to update plot
data
"""
import pygtk
pygtk.require('2.0')
import gtk
from gtk import gdk
import matplotlib
matplotlib.use('GTKAgg') # or 'GTK'
from matplotlib.backends.backend_gtk import FigureCanvasGTK as FigureCanvas
from numpy.random import random
from matplotlib.figure import Figure
我尝试在终端中运行此脚本我收到以下错误:
Traceback (most recent call last):
File "gtk_spreadsheet.py", line 15, in <module>
from matplotlib.backends.backend_gtk import FigureCanvasGTK …