Artificial data generation with numpy
You can output in csv format and scatter plot. The c_ and r_ operators are used.
Artificial data that can be created Circles and their combinations (circles in circles (2D), xor-like patterns (2D), intertwined circles (3D)) Twisted and intertwined ring (3D) Spherical shell of any dimension (spherical shell in the spherical shell) Lorenz attractor Rossler attractor
reference Marsaglia's method http://stackoverflow.com/questions/15880367/python-uniform-distribution-of-points-on-4-dimensional-sphere
gendata.py
# -*- coding: utf-8 -*-
"""
Created on Wed May 07 21:17:21 2014
@author: xiangze
"""
import csv
import numpy as np
#from matplotlib.pyplot import *
import matplotlib.pyplot as plt
PI=np.pi
PI2=2*PI
def gencircle(rc,rr=0.1,offset=[0,0],num=100,label=0):
c=[]
for i in range(num):
r=rc+np.random.uniform(-rr,rr,1)
th=np.random.uniform(0,PI2,1)
c.append([r*np.sin(th)+offset[0],r*np.cos(th)+offset[1]])
return np.c_[np.array(c).reshape(num,2),np.repeat(label,num)]
def genring(rc,rr=0.1,offset=[0,0,0],num=100,label=0,normaldir='x'):
if(normaldir=='x'):
a=gencircle(rc,rr,[offset[1],offset[2]],num,label)
return np.c_[np.repeat(offset[0],num),a[:,0],a[:,1],a[:,2]]
elif(normaldir=='y'):
a=gencircle(rc,rr,[offset[0],offset[2]],num,label)
return np.c_[a[:,0],np.repeat(offset[1],num),a[:,1],a[:,2]]
else:
a=gencircle(rc,rr,[offset[0],offset[1]],num,label)
return np.c_[a[:,0],a[:,1],np.repeat(offset[2],num),a[:,2]]
def gentwistedring0(rc=[1,0.3],rr=0.1,offset=[0,0,0],num=100,label=0,twistratio=3.0,phase=0):
c=[]
for i in range(num):
r=rc[0]+np.random.uniform(-rr,rr,1)
th=np.random.uniform(0,PI2,1)
c1=[r*np.sin(th)+offset[0],r*np.cos(th)+offset[1],offset[2]]
c2=[rc[1]*np.sin(th*twistratio+phase)*np.sin(th) , rc[1]*np.sin(th*twistratio+phase)*np.cos(th) ,rc[1]*np.cos(th*twistratio+phase)]
c.append([c1[i]+c2[i] for i in range(len(c1))])
return np.c_[np.array(c).reshape(num,3),np.repeat(label,num)]
def gentwistedring(rc=[1,0.3],rr=0.1,offset=[0,0,0],num=100,label=0,normaldir='x',twistratio=5.0,phase=0):
a=gentwistedring0(rc,rr,offset,num,label,twistratio,phase)
if(normaldir=='x'):
return a
elif(normaldir=='y'):
return np.c_[a[:,1],a[:,2],a[:0],a[:3]]
else:
return np.c_[a[:,2],a[:,0],a[:1],a[:3]]
#http://stackoverflow.com/questions/15880367/python-uniform-distribution-of-points-on-4-dimensional-sphere
#Marsaglia's method
def gensphere(rc,rr=0.1,offset=[0,0,0],num=100,label=0,dim=3):
normal_deviates = np.random.normal(size=(dim, num))
r=rc+np.random.uniform(-rr,rr,1)
r = np.sqrt((normal_deviates**2).sum(axis=0))*r
p =normal_deviates/r
return np.c_[np.array(zip(*p)).reshape(num,dim),np.repeat(label,num)]
def gensphere0(rc,rr=0.1,offset=[0,0,0],num=100,label=0):
c=[]
n=int(np.sqrt(num))
for ph in np.random.uniform(-PI,PI,n):
for th in np.random.uniform(0,PI2,n):
r=rc+np.random.uniform(-rr,rr,1)
c.append([r*np.sin(th)*np.sin(ph)+offset[0],r*np.cos(th)*np.sin(ph)+offset[1],r*np.cos(ph)+offset[2]])
return np.c_[np.array(c).reshape(num,3),np.repeat(label,num)]
def gensphere1(rc,rr=0.1,offset=[0,0,0],num=100,label=0):
c=[]
n=int(np.sqrt(num))
for ph in np.random.uniform(-PI,PI,n):
p=0
if(p>=n):
break
else:
m=int(np.abs(np.sin(ph)*n))
if(m!=0):
for th in np.random.uniform(0,PI2,m):
r=rc+np.random.uniform(-rr,rr,1)
c.append((r*np.sin(th)*np.sin(ph)+offset[0],r*np.cos(th)*np.sin(ph)+offset[1],r*np.cos(ph)+offset[2]))
p=p+m
l=len(c)
return np.c_[np.array(c).reshape(l,3),np.repeat(label,l)]
def genlorenz(init=[0,0.1,0],offset=[0,0,0],rr=0.,num=100,p=10,r=28,b=2.66,label=0,dt=0.01):
cc=[]
x=init[0]
y=init[1]
z=init[2]
for t in range(num):
cc.append([x,y,z])
x=x+dt*(-p*x+p*y) +np.random.uniform(-rr,rr,1)
y=y+dt*(-x*z+r*x-y) +np.random.uniform(-rr,rr,1)
z=z+dt*( x*y-b*z) +np.random.uniform(-rr,rr,1)
return np.c_[np.array(cc).reshape(num,3),np.repeat(label,num)]
def genrossler(init=[0,5,0],offset=[0,0,0],num=100,a=0.2,b=0.2,c=5.7,label=0,dt=0.05):
cc=[]
x=init[0]
y=init[1]
z=init[2]
for t in range(num):
cc.append([x,y,z])
x=x+dt*(-y-z)
y=y+dt*( x+a*y)
z=z+dt*( b+z*(x-c))
return np.c_[np.array(cc).reshape(num,3),np.repeat(label,num)]
def cshow2(data):
cc=zip(*data)
plt.scatter(cc[0],cc[1],c=cc[2])
plt.draw()
plt.show()
def cshow3(data):
from mpl_toolkits.mplot3d import Axes3D
fig=plt.figure()
ax = Axes3D(fig)
cc=zip(*data)
ax.scatter(cc[0],cc[1],cc[2],c=cc[3])
plt.draw()
plt.show()
def test(data,dump=False,fname="test.csv"):
if(data.shape[1]==3):
cshow2(data)
else:
cshow3(data)
if(dump):
np.savetxt(fname,data,delimiter=",")
if __name__=="__main__":
num=200
circles=np.vstack([gencircle(1,0.1,num=num,label=0),gencircle(1,0.1,[-2,2],num=num,label=1)])
test(circles)
#circle in circle
cinc=np.r_[gencircle(1,0.1,num=num,label=0),gencircle(2,0.1,num=num,label=1)]
test(cinc)
#XOR-like pattern
xor0=np.r_[gencircle(0.5,num=num/2,offset=[0,0],label=0),gencircle(0.5,offset=[1,1],label=0)]
xor1=np.r_[gencircle(0.5,num=num/2,offset=[0,1],label=1),gencircle(0.5,offset=[1,0],label=1)]
xor=np.r_[xor0,xor1]
test(xor)
#3D ring
rings=np.r_[genring(1,0.1,num=num,offset=[0,0,0],label=0,normaldir='x'),\
genring(1,0.1,num=num,offset=[0,0,1],label=1,normaldir='y')]
test(rings)
num=400
#sphere in sphere
sins=np.r_[gensphere(1,num=num,label=0),gensphere(2,num=num,label=1)]
test(sins)
#twisted rings
test(np.vstack([gentwistedring(num=num,label=0),gentwistedring(num=num,label=1,phase=PI)]))
num=1000
rossler=genrossler(num=num,dt=0.1)
test(rossler)
lorenz=genlorenz(num=num,dt=0.05)
test(lorenz)
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