投资组合方差的计算方法为:
port_var = W'_p * S * W_p
对于具有N个评估的投资组合,其中
W'_p = transpose of vector of weights of stocks in portfolios
S = sample covariance matrix
W_p = vector of weights of stocks in portfolios
我有下面的numpy矩阵。
投资组合中股票权重的数组(向量)(共有10只股票):
weights = np.array(
[[ 0.09],
[ 0.05],
我写这段代码是为了对给定的图像进行均值过滤。在它中,我首先初始化一个二维数组。但是,当我尝试将一个值赋给特定的单元格时,它实际上将该值赋给了整个列。如下所示:
def boxBlur(image):
height = len(image)
width = len(image[0])
result = [[0]*(width-2)]*(height-2)
for i in range(height-2):
for j in range(width-2):
mysum
我用一个函数来计算一个似然密度。
我正在运行两个x,它们是长度为7的向量。
def lhd(x0, x1, dt): #Define a function to calculate the likelihood density given two values.
d = len(x0) #Save the length of the inputs for the below pdf input.
print(d)
print(len(x1))
lh = multivariate_normal.pdf(x1, mean=(1-dt)*x0, cov=2*dt*n
我正在编写一些遗留代码。我有以下数据类型:
typedef struct {
char *name ;
ColumnType type ;
unsigned pos ; //column position in table
CellData **data ; //ptr to list of cells in column
}Column ;
struct _table {
char name[TABLE_NAME_LEN+1] ;
unsigned int num_rows ;
uns
import numpy as np
import scipy as sc
from sklearn.preprocessing import normalize
import scipy.sparse as sp
import numpy
import numpy as np
import scipy.sparse as sp
def func1(A,c,eps,maxiter):
c=0.8
eps=1e-4
maxiter=20
n=sc.shape(A)[0]
sim=sc.eye(n)
si
我对这样的错误有问题:运行时检查失败#2 -在变量'numb‘周围的堆栈已经损坏。它出现在这类职能的最后一个括号中:
int problem20()
{
int res = 0, i;
int numb[160];
for(i = 0; i < 160; i++)
numb[i] = 0;
numb[0] = 1;
for(i = 1; i < 100; i++)
{
multiply(numb, i, numb, 160);
}
for(i = 0; i < 160; i
我试着画出如下图:
x=0:0.1:1;
plot(x,2*x-x^2);
为什么会出现以下错误:-
Error using ^
Inputs must be a scalar and a square matrix.
To compute elementwise POWER, use POWER (.^) instead.
其目标是仅绘制二次函数。所以我修改了上面的代码如下:
x=0:0.1:1;
plot(x,2*x-x*x);
错误仍然存在:-
Error using *
Inner matrix dimensions must agree.
我哪里错了?