Python:ValueError: 形状 (3,) 和 (118,1) 未对齐：3 (dim 0) != 118 (dim 0)
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Python:ValueError: shapes (3,) and (118,1) not aligned: 3 (dim 0) != 118 (dim 0)
提问by Sahil Dahiya
I am trying to do logistic regression using fmin but there is an error showing up due to different shapes of array. Here is the code.
我正在尝试使用 fmin 进行逻辑回归，但由于数组形状不同，出现错误。这是代码。
import numpy as np
import scipy.optimize as sp
data= #an array of dim (188,3)
X=data[:,0:2]
y=data[:,2]
m,n=np.shape(X)
y=y.reshape(m,1)
x=np.c_[np.ones((m,1)),X]
theta=np.zeros((n+1,1))
def hypo(x,theta):
return np.dot(x,theta)
def sigmoid(z):
return 1/(1+np.exp(-z))
def gradient(x,y,theta):#calculating Gradient
m=np.shape(x)[0]
t=hypo(x,theta)
hx=sigmoid(t)
J=-(np.dot(np.transpose(np.log(hx)),y)+np.dot(np.transpose(np.log(1-hx)),(1-y)))/m
grad=np.dot(np.transpose(x),(hx-y))/m
J= J.flatten()
grad=grad.flatten()
return J,grad
def costFunc(x,y,theta):
return gradient(x,y,theta)[0]
def Grad():
return gradient(x,y,theta)[1]
sp.fmin( costFunc, x0=theta, args=(x, y), maxiter=500, full_output=True)
error that is showing
显示的错误
File "<ipython-input-3-31a0d7ca38c8>", line 35, in costFunc
return gradient(x,y,theta)[0]
File "<ipython-input-3-31a0d7ca38c8>", line 25, in gradient
t=hypo(x,theta)
File "<ipython-input-3-31a0d7ca38c8>", line 16, in hypo
return np.dot(x,theta)
ValueError: shapes (3,) and (118,1) not aligned: 3 (dim 0) != 118 (dim 0)
Any kind of help will be appreciated
任何形式的帮助将不胜感激
采纳答案by hpaulj
data= #an array of dim (188,3)
X=data[:,0:2]
y=data[:,2]
m,n=np.shape(X)
y=y.reshape(m,1)
x=np.c_[np.ones((m,1)),X]
theta=np.zeros((n+1,1))
so after this
所以在这之后
In [14]: y.shape
Out[14]: (188, 1) # is this (118,1)?
In [15]: x.shape
Out[15]: (188, 3)
In [16]: theta.shape
Out[16]: (3, 1)
This xand thetacan dotted- np.dot(x,theta), and (188,3) with (3,1) - matching the 3's.
这x和theta可以dotted- np.dot(x,theta)，和 (188,3) 与 (3,1) - 匹配 3。
But that's not what your costFuncis getting. Tracing back from the error message it looks like xis (3,), and thetais (118,1). which obviously cannot be dotted.
但这不是你costFunc得到的。从错误消息中回溯它看起来x是(3,)，并且theta是(118,1)。这显然不能dotted。
You need to review how fmincalls your function. Do you have the parameters in the right order? For example, maybe costFunc(theta, x, y)is the correct order (assuming the xand yin costFuncare meant to match with the args=(x,y).
您需要查看如何fmin调用您的函数。你有正确顺序的参数吗？例如，也许costFunc(theta, x, y)是正确的顺序（假设x和yincostFunc意在与args=(x,y).
The docs for fmininclude:
文档fmin包括：
func : callable func(x,*args) The objective function to be minimized. x0 : ndarray Initial guess. args : tuple, optional Extra arguments passed to func, i.e. ``f(x,*args)``.
func : callable func(x,*args) The objective function to be minimized. x0 : ndarray Initial guess. args : tuple, optional Extra arguments passed to func, i.e. ``f(x,*args)``.
It looks like fminis feeding your costFunc3 arguments, corresponding in size to your (theta, x, y), i.e. (3,), (118,3), (118,1). The numbers don't quite match, but I think you get the idea. The first argument to consFuncis the one that the fminwill vary, the rest you provide in args.
看起来像是fmin在提供您的costFunc3 个参数，它们的大小对应于您的(theta, x, y), 即(3,), (118,3), (118,1)。数字不太匹配，但我想你明白了。第一个参数consFunc是fmin将变化的，其余的你在args.
