Help us understand the problem. What is going on with this article?

# 1.要約

前回の記事で，収束判定を"尤度の値が変わらなくなるまで"と"係数の値が変わらなくなるまで"の2パターン紹介しました．今回の記事は，後者の収束基準に基づくプログラムを載せています．詳しい数理は前回の記事をご覧ください．

# 2.サンプルコード

```def calc_p(beta,MatX):
a,b = MatX.shape
pxb = np.zeros((a,1))
for i in range(a):
pxb[i,0] = np.exp(beta.T.dot(MatX[i,:]))/(1+np.exp(beta.T.dot(MatX[i,:])))
#pxb[i,0] = np.exp(beta.T.dot(MatX[i,:].reshape(b,1)))/(1+np.exp(beta.T.dot(MatX[i,:].reshape(b,1))))
return pxb

def losgistic_estimation2(y,data,tol=10**(-4),nstart=30,maxiter=50):
X = data.astype("float64");n,p = X.shape
X1 = np.hstack([np.ones(n).reshape(n,1),X]).reshape(n,p+1)
y = y.astype("float64").reshape(n,1)
beta_hat = np.zeros(((p+1),1))
delta_hat = np.Inf
for _ in range(nstart):
print("---epoc:%d ---" % (_+1))
#initial beta
beta = np.random.randn(p+1).reshape((p+1),1)
delta = np.Inf
while True:
print("  delta: %f" % delta)
#Newton-Raphson step
prob = calc_p(beta,X1)
#print(prob) #for check
W = np.zeros((n,n))
for i in range(n):
W[i,i] = prob[i,0]*(1-prob[i,0])
#print(W) # for check
pd2 = -X1.T.dot(W).dot(X1)
pd1 = X1.T.dot(y-prob)
new_beta = beta - np.linalg.inv(pd2).dot(pd1)
print("new_beta: ",new_beta)
delta = sum((beta - new_beta)**2)
if delta >= 0 and delta <= tol:
print("parameters are converged")
break
elif np.isnan(delta)==True:
break
else:
beta = new_beta
continue
if np.isnan(delta)==True:
print("ERROR:delta is nan \nStop and go next epoc")
continue
if delta < delta_hat:
beta_hat = new_beta
return beta_hat

# データ行列の生成
N = 1000;p = 2
np.random.seed(10)
X = np.random.randn(N*p).reshape(N,p)
X1 = np.hstack([np.ones(N).reshape(N,1),X])
#正解ラベルの生成
y = np.zeros((N,1))
np.random.seed(100)
beta = np.random.randn(p+1).reshape(p+1,1)
prob = np.exp(X1.dot(beta))/(1+np.exp(X1.dot(beta)))
prob
for i in range(N):
np.random.seed(i+8)
if np.random.rand(1) < prob[i]:
y[i] = 1
else:
y[i] = 0
y

losgistic_estimation2(y,X)
beta

```
Why not register and get more from Qiita?
1. We will deliver articles that match you
By following users and tags, you can catch up information on technical fields that you are interested in as a whole
2. you can read useful information later efficiently
By "stocking" the articles you like, you can search right away