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"""
Simple example datasets for OT
"""
import numpy as np
import scipy as sp
def get_1D_gauss(n,m,s):
"""return a 1D histogram for a gaussian distribution (n bins, mean m and std s)
Parameters
----------
n : int
number of bins in the histogram
m : float
mean value of the gaussian distribution
s : float
standard deviaton of the gaussian distribution
Returns
-------
h : np.array (n,)
1D histogram for a gaussian distribution
"""
x=np.arange(n,dtype=np.float64)
h=np.exp(-(x-m)**2/(2*s^2))
return h/h.sum()
def get_2D_samples_gauss(n,m,sigma):
"""return n samples drawn from 2D gaussian N(m,sigma)
Parameters
----------
n : int
number of bins in the histogram
m : np.array (2,)
mean value of the gaussian distribution
sigma : np.array (2,2)
covariance matrix of the gaussian distribution
Returns
-------
X : np.array (n,2)
n samples drawn from N(m,sigma)
"""
if np.isscalar(sigma):
sigma=np.array([sigma,])
if len(sigma)>1:
P=sp.linalg.sqrtm(sigma)
res= np.random.randn(n,2).dot(P)+m
else:
res= np.random.randn(n,2)*np.sqrt(sigma)+m
return res
def get_data_classif(dataset,n,nz=.5,**kwargs):
""" dataset generation for classification problems
Parameters
----------
dataset : str
type of classification problem (see code)
n : int
number of training samples
nz : float
noise level (>0)
Returns
-------
X : np.array (n,d)
n observation of size d
y : np.array (n,)
labels of the samples
"""
if dataset.lower()=='3gauss':
y=np.floor((np.arange(n)*1.0/n*3))+1
x=np.zeros((n,2))
# class 1
x[y==1,0]=-1.; x[y==1,1]=-1.
x[y==2,0]=-1.; x[y==2,1]=1.
x[y==3,0]=1. ; x[y==3,1]=0
x[y!=3,:]+=1.5*nz*np.random.randn(sum(y!=3),2)
x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2)
elif dataset.lower()=='3gauss2':
y=np.floor((np.arange(n)*1.0/n*3))+1
x=np.zeros((n,2))
y[y==4]=3
# class 1
x[y==1,0]=-2.; x[y==1,1]=-2.
x[y==2,0]=-2.; x[y==2,1]=2.
x[y==3,0]=2. ; x[y==3,1]=0
x[y!=3,:]+=nz*np.random.randn(sum(y!=3),2)
x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2)
else:
x=0
y=0
print("unknown dataset")
return x,y.astype(int)
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