[英]Logistic regression: objects are not aligned
I am trying to do logistic regression on this dataset from A Ng's machihne learning class in coursera. 我正在尝试从Coursera的A Ng的machihne学习班对此数据集进行逻辑回归。
The idea is that we have a cost function, which we need to minimize to find the parameters theta. 这个想法是我们有一个成本函数,我们需要将其最小化以找到参数theta。
import numpy as np
from scipy.optimize import fmin_bfgs
data = np.loadtxt('ex2data1.txt',delimiter=",")
m,n = data.shape
X = np.array(np.column_stack((np.ones(m),data[:,:-1])))
y = np.array(data[:,2].reshape(m,1))
theta = np.array(np.zeros(n).reshape(n,1))
def sigmoid(z):
return 1/(1+np.exp(-z))
def hypothesis(X,theta):
return sigmoid( X.dot(theta) )
def cost(theta):
print theta.shape
h = hypothesis(X,theta)
cost = (-y.T.dot(np.log(h))-(1-y).T.dot(np.log(1-h)))/m
return cost
def gradient(theta):
h = hypothesis(X,theta)
grad = ((h-y).T.dot(X)).T/m
return grad.flatten()
def fmin():
initial_theta=np.zeros(n).reshape(n,1)
theta=fmin_bfgs(cost,initial_theta,fprime=gradient)
return theta
print fmin() 打印fmin()
I am getting ValueError: Objects are not aligned
but I have checked the shapes of all entities and still can't figure it out. 我收到ValueError: Objects are not aligned
但是我检查了所有实体的形状,但仍然无法弄清楚。 Here is the traceback: 这是回溯:
---> 32 theta=fmin_bfgs(cost,initial_theta,fprime=gradient)
33
/usr/lib/python2.7/dist-packages/scipy/optimize/optimize.pyc in fmin_bfgs(f, x0, fprime, args, gtol, norm, epsilon, maxiter, full_output, disp, retall, callback)
775 'return_all': retall}
776
--> 777 res = _minimize_bfgs(f, x0, args, fprime, callback=callback, **opts)
778
779 if full_output:
/usr/lib/python2.7/dist-packages/scipy/optimize/optimize.pyc in _minimize_bfgs(fun, x0, args, jac, callback, gtol, norm, eps, maxiter, disp, return_all, **unknown_options)
844 gnorm = vecnorm(gfk, ord=norm)
845 while (gnorm > gtol) and (k < maxiter):
--> 846 pk = -numpy.dot(Hk, gfk)
847 try:
848 alpha_k, fc, gc, old_fval, old_old_fval, gfkp1 = \
ValueError: objects are not aligned
I modified your code, it can get the same result as LogisticRegression in sklearn with c=inf
: 我修改了您的代码,使用c=inf
可以得到与sklearn中的LogisticRegression相同的结果:
import numpy as np
from scipy.optimize import fmin_bfgs
import io
data = np.loadtxt('ex2data1.txt',delimiter=",")
m,n = data.shape
X = np.array(np.column_stack((np.ones(m),data[:,:-1])))
y = np.array(data[:,2].reshape(m,1))
theta = np.array(np.zeros(n).reshape(n,1))
def sigmoid(z):
return 1/(1+np.exp(-z))
def hypothesis(X,theta):
return sigmoid( X.dot(theta) )
def cost(theta):
h = hypothesis(X,theta)
cost = (-y.T.dot(np.log(h))-(1-y).T.dot(np.log(1-h)))/m
r = cost[0]
if np.isnan(r):
return np.inf
return r
def gradient(theta):
theta = theta.reshape(-1, 1)
h = hypothesis(X,theta)
grad = ((h-y).T.dot(X)).T/m
return grad.flatten()
def fmin():
initial_theta=np.zeros(n)
theta=fmin_bfgs(cost,initial_theta,fprime=gradient)
return theta
theta = fmin()
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