Prob 4-1¶
For every order, you have to do the cross-validation to choose the best model. You need to record the validation error (27 values) and assign on your report. Use the best model to test the training set (10 point) and the test set, you would get training error and the test error for the order. Draw the figure (error vs order) with the training and test error.
In [1]:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib
import matplotlib .pyplot as plt
In [2]:
data4 = pd.read_csv("x3t3.csv")
建立Model¶
In [3]:
def basis_func(x,deg): #x=[x0] np.array #一筆資料
X = []
feature_names = []
for i in range(deg+1):
X.append(x**i)
feature_names.append('x^'+str(i))
return X , feature_names
In [4]:
def buildup_basis_func_matrix(train_x,deg): # deg:degree(order)
N = train_x.size / train_x[0].size
#N=2
MX = [] #matrix of x
F = [] #feature_names
for i in range(N):
r = np.array(train_x[i])[0] #取出某一row, 因是matrix形式要取list
#print r
X = []
X,F = basis_func(r,deg) #丟進基底函數
MX.append(X)
return np.matrix(MX),F # matrix of fi(x), feature_names
In [5]:
def buildup_model_no_regularization(train_x,train_y,deg):
MX,F = buildup_basis_func_matrix(train_x,deg)
#return MX,F
psX = np.linalg.pinv(MX) #算 pseudo-inverse
Y = train_y #Y=[[y0],[y1]...] target/label,column matrix
#print psX
W = np.dot(psX,Y) #weight W = [ [w0] ,[w1]...] , column matrix
return W,F
In [6]:
def predict_model_no_regularization(test_x,W,deg): # weight W = [ [w0] ,[w1]...] 是train好的 Weight, column matrix
MX,F = buildup_basis_func_matrix(test_x,deg)
#print MX
#print W
pred_y = np.dot(MX,W)
return pred_y # pred_y is column matrix
In [7]:
def colMatrix2Array(colM): # column matrix (np.marix) --> (np.array)
L = np.array(colM.reshape(1,colM.size).tolist()[0])
return L, L.size
def L1_norm(W): # W: column matrix (np.marix) -> |W|
return np.sum(np.abs(W))
print L1_norm(np.matrix([[-12],[11]]))
def L2_norm(W): # W: column matrix (np.marix) -> |W|^2
return np.sum(np.square(W))
print L2_norm(np.matrix([[-12],[11]]))
def calError_model(pred_y,test_y,norm,lenda,W): #pred_y,test_y, W 都是column matrix
py, N = colMatrix2Array(pred_y)
ty, N = colMatrix2Array(test_y)
sum_square_diff = np.sum( np.square( py-ty ) )
E_D = 0.5* sum_square_diff
RMS = np.sqrt(sum_square_diff/N)
#print N,sum_square_diff
if norm == 0: # norm = 0 or lenda=0 means no_regularization
return E_D, E_D, 0, RMS
elif norm == 1:
E_W = 0.5*lenda*L1_norm(W)
return (E_D+E_W), E_D, E_W, RMS
elif norm == 2:
E_W = 0.5*lenda*L2_norm(W)
return (E_D+E_W), E_D, E_W, RMS
else:
return -1, -1, -1, -1
23 265
Validation¶
In [8]:
from sklearn.model_selection import KFold #此功能只是用來split validation sets, 沒有參與model建立
def Reg_CV(degree): #cross_validation, 挑某一個當作validation set, 而其他的用來training
#---將training set切割成三個子集, 輪流當validation set---
kf = KFold(n_splits=3)
V_Err = []
for train, test in kf.split(range(15)):
train_x = np.array(data4.train_x[train]).reshape(-1,1)
train_y = np.array(data4.train_y[train]).reshape(-1,1)
test_x = np.array(data4.train_x[test]).reshape(-1,1)
test_y = np.array(data4.train_y[test]).reshape(-1,1)
#print train_x
#---------build up model & find coeff---------------------------
W,feature_names = buildup_model_no_regularization(train_x,train_y,degree)
coef,fn = colMatrix2Array(W)
coef_pair = zip(np.abs(coef),range(fn))
#---------predict y from test_x---------------------
pred_y = predict_model_no_regularization(test_x,W,degree)
E, E_D, E_W, RMS_test = calError_model(pred_y,test_y,0,0,W) # norm = 0 , lenda=0 means no_regularization
V_Err.append(E_D)#validation error
return V_Err
Reg_CV(2)
Out[8]:
[1.9485737666954237, 8.3723403959957903, 6.7334056150610877]
In [9]:
D = [] #order/degree
E = [] #Valid_Error
for deg in range(1,10):
D.append(deg)
E.append ( np.log(Reg_CV(deg)) )
Err = pd.DataFrame(np.matrix(E),index=['deg_'+str(i) for i in range(1,10)],
columns=['valid_set'+str(i) for i in range(1,4)],dtype=float)
Err['AVG_Valid_Err'] = [ Err.iloc[i,:].sum()/3 for i in range(0,9)] #計算平均valid_err for each degree
Err.to_csv('Prob4_valid_err.csv')
Err
Out[9]:
| valid_set1 | valid_set2 | valid_set3 | AVG_Valid_Err | |
|---|---|---|---|---|
| deg_1 | 0.596384 | 1.167136 | 1.899200 | 1.220906 |
| deg_2 | 0.667098 | 2.124933 | 1.907081 | 1.566371 |
| deg_3 | 0.009065 | 0.953747 | 1.546981 | 0.836598 |
| deg_4 | 0.379446 | 1.210181 | 0.879537 | 0.823055 |
| deg_5 | 0.254716 | 0.180714 | 1.068086 | 0.501172 |
| deg_6 | 0.602310 | 7.208708 | 4.690566 | 4.167195 |
| deg_7 | 0.901676 | 10.015391 | 8.729197 | 6.548755 |
| deg_8 | 0.668579 | 17.883607 | 12.002270 | 10.184819 |
| deg_9 | 10.614027 | 17.612933 | 18.489902 | 15.572287 |
In [10]:
plt.plot(D, Err['valid_set1'], 'r--', D, Err['valid_set2'], 'g--',
D, Err['valid_set3'], 'y--', D, Err['AVG_Valid_Err'], 'b-',linewidth=2)
plt.title('order vs. valid_err, red:valid_set1, green:valid_set2, yellow:valid_set3, blue:avg ')
plt.xlabel('order M')
plt.ylabel('log(valid_err)')
plt
Out[10]:
<module 'matplotlib.pyplot' from 'C:\Users\Phoebe Huang\Anaconda2\lib\site-packages\matplotlib\pyplot.pyc'>
Testing¶
In [11]:
def Reg(train_x, train_y,test_x,test_y,degree): #train -> test
#---------build up model & find coeff---------------------------
W,feature_names = buildup_model_no_regularization(train_x,train_y,degree)
coef,fn = colMatrix2Array(W)
coef_pair = zip(np.abs(coef),range(fn))
#---------predict y from test_x---------------------
pred_y = predict_model_no_regularization(test_x,W,degree)
E, E_D, E_W, RMS_test = calError_model(pred_y,test_y,0,0,W)# norm = 0 , lenda=0 means no_regularization
#---------plot---------------------------
linX = np.linspace(0,6,1000).reshape(-1,1)
linY = predict_model_no_regularization(linX,W,degree)
plt.plot(test_x,test_y,'ro', linX, linY, 'b-')
plt.title('Degree='+str(degree))
plt.xlabel('X')
plt.ylabel('Y')
#plt.show()
plt.savefig('prob4_fig/deg'+str(degree)+'.jpg')
plt.close()
return E
In [12]:
train_x_all = np.array(data4.train_x).reshape(-1,1)
train_y_all = np.array(data4.train_y).reshape(-1,1)
test_x_all = np.array(data4.test_x[0:10]).reshape(-1,1)
test_y_all = np.array(data4.test_y[0:10]).reshape(-1,1)
In [13]:
D = [] #order/degree
T_E = [[],[]] #Error function for Training/Testing set
max_deg = 12
for deg in range(1,max_deg+1,1):
D.append(deg)
T_E[0].append (Reg(train_x_all,train_y_all,train_x_all,train_y_all,deg))
T_E[1].append (Reg(train_x_all,train_y_all,test_x_all,test_y_all,deg))
#print T_E
T_E.append(D)
Err = pd.DataFrame(np.matrix(T_E).transpose(),index=D,
columns=['Training set','Testing set','degree'])
Err['AVG_test_Err'] = (Err['Training set']+Err['Testing set'])/2 #計算平均test_err for each degree
Err.to_csv('Prob4_test_err.csv')
Err
Out[13]:
| Training set | Testing set | degree | AVG_test_Err | |
|---|---|---|---|---|
| 1 | 8.571835 | 26.843082 | 1.0 | 17.707458 |
| 2 | 8.286504 | 25.546623 | 2.0 | 16.916563 |
| 3 | 4.536034 | 14.229472 | 3.0 | 9.382753 |
| 4 | 3.142099 | 12.037603 | 4.0 | 7.589851 |
| 5 | 2.350315 | 10.938616 | 5.0 | 6.644466 |
| 6 | 2.320304 | 9.968271 | 6.0 | 6.144288 |
| 7 | 2.318721 | 9.564454 | 7.0 | 5.941588 |
| 8 | 2.237168 | 12.549179 | 8.0 | 7.393173 |
| 9 | 2.124401 | 13.195952 | 9.0 | 7.660176 |
| 10 | 1.806509 | 6.998458 | 10.0 | 4.402484 |
| 11 | 1.195913 | 349.929877 | 11.0 | 175.562895 |
| 12 | 1.065496 | 2380.016056 | 12.0 | 1190.540776 |
In [14]:
plt.plot( D, np.log(Err['Training set']), 'b-',D, np.log(Err['Testing set']), 'r-',linewidth=2)
plt.title('order vs. Error, blue:Train, red:Test')
plt.xlabel('order M')
plt.ylabel('log(Error)')
Out[14]:
<matplotlib.text.Text at 0xf682518>
Prob 4-2¶
In [15]:
def buildup_model_L2_regularization(train_x,train_y,deg,lenda):
MX,F = buildup_basis_func_matrix(train_x,deg)
#return MX,F
#***********************************************************
XT = np.transpose(MX)
psX = np.linalg.inv(np.add(lenda*np.eye(deg+1),np.dot(XT,MX)))*XT
#***********************************************************
Y = train_y #Y=[[y0],[y1]...] target/label,column matrix
#print psX
W = np.dot(psX,Y) #weight W = [ [w0] ,[w1]...] , column matrix
return W,F
In [16]:
def predict_model_L2_regularization(test_x,W,deg): # weight W = [ [w0] ,[w1]...] 是train好的 Weight, column matrix
MX,F = buildup_basis_func_matrix(test_x,deg)
#print MX
#print W
pred_y = np.dot(MX,W)
return pred_y # pred_y is column matrix
In [17]:
def Reg_L2(train_x, train_y,test_x, test_y,degree,lenda,if_plot_coeff):
#---------build up model & find coeff---------------------------
W,feature_names = buildup_model_L2_regularization(train_x,train_y,degree,lenda)
coef,fn = colMatrix2Array(W)
coef_pair = zip(np.abs(coef),range(fn))
print 'feature_names=',feature_names
print 'coef=',coef
#---------plot coeff---------------------------
if if_plot_coeff:
print 'plot:'
plt.plot(range(fn),np.abs(coef),'b-')
plt.xlabel('coef order')
plt.ylabel('abs(w)')
plt.title('Coef Weight')
plt.show()
#plt.close()
#---------predict y from test_x---------------------
pred_y = predict_model_L2_regularization(test_x,W,degree)
E, E_D, E_W, RMS_test = calError_model(pred_y,test_y,2,lenda,W)# norm = 0 , lenda=0 means no_regularization
print 'E, E_D, E_W=',E, E_D, E_W
return E
In [18]:
train_x = np.array(data4.train_x).reshape(-1,1)
train_y = np.array(data4.train_y).reshape(-1,1)
test_x = np.array(data4.test_x[0:10]).reshape(-1,1)
test_y = np.array(data4.test_y[0:10]).reshape(-1,1)
train_x.size
Out[18]:
15
In [19]:
%matplotlib inline
degree = 9
L = [] #lenda
E_T = [] #Error for sum
min_e = [0,350000] # i,j,e_t,e_d,e_w
for j in range(0,100,10): #設定lenda範圍
l = (float)(j)/100
print 'Lambda =', l
print ''
e = Reg_L2(train_x, train_y,test_x, test_y, degree, l, True)
L.append( l )
E_T.append( e )
print 'Err =', [l,e]
if min_e[1] > e :
min_e = [l,e]
print ''
print "*" *64
print ''
print "min_e = " , min_e # 算出[0.3, 8.7275293447745863] 與原本答案[0.3, 8.7275410196083474]很接近
Lambda = 0.0 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ 4.27193960e-01 -6.92818951e+00 -4.00694905e+00 3.39092488e+01 -3.95555412e+01 2.14835691e+01 -6.43315355e+00 1.09151979e+00 -9.83743869e-02 3.66020123e-03] plot:
E, E_D, E_W= 13.2100041132 13.2100041132 0.0 Err = [0.0, 13.210004113245109] **************************************************************** Lambda = 0.1 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -3.25005371e-02 -6.90595745e-01 -1.84824864e-01 1.82289460e-01 2.39801490e-01 4.16002328e-02 -1.41921547e-01 5.21693668e-02 -7.37504769e-03 3.71053587e-04] plot:
E, E_D, E_W= 8.75247421561 8.72109813175 0.0313760838584 Err = [0.1, 8.7524742156106203] **************************************************************** Lambda = 0.2 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.53750764e-02 -5.26785145e-01 -1.88927068e-01 1.06154491e-01 2.21757080e-01 9.12059584e-02 -1.64549003e-01 5.68589988e-02 -7.84679411e-03 3.89763503e-04] plot:
E, E_D, E_W= 8.72876581224 8.6871053646 0.0416604476406 Err = [0.2, 8.7287658122385192] **************************************************************** Lambda = 0.3 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.38434168e-02 -4.41112786e-01 -1.78500826e-01 7.42942548e-02 2.05666018e-01 1.10302458e-01 -1.70120775e-01 5.74716898e-02 -7.85749423e-03 3.88255055e-04] plot:
E, E_D, E_W= 8.72754101961 8.67867649504 0.0488645245643 Err = [0.3, 8.7275410196085232] **************************************************************** Lambda = 0.4 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.57644746e-02 -3.85461178e-01 -1.66444195e-01 5.80885332e-02 1.93147629e-01 1.17671948e-01 -1.69849494e-01 5.67750125e-02 -7.72675363e-03 3.80758757e-04] plot:
E, E_D, E_W= 8.73222108811 8.67779830885 0.0544227792618 Err = [0.4, 8.7322210881147662] **************************************************************** Lambda = 0.5 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [-0.10387935 -0.34522125 -0.15491846 0.04896682 0.1831177 0.11987693 -0.16730764 0.0556159 -0.00755001 0.00037145] plot:
E, E_D, E_W= 8.73841346065 8.67956076097 0.0588526996748 Err = [0.5, 8.7384134606478749] **************************************************************** Lambda = 0.6 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [-0.1095805 -0.31417178 -0.14438723 0.04352198 0.17482896 0.11959341 -0.16386829 0.05430902 -0.00736188 0.00036182] plot:
E, E_D, E_W= 8.74486419021 8.68241079261 0.062453397598 Err = [0.6, 8.7448641902061546] **************************************************************** Lambda = 0.7 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [-0.11357781 -0.28919109 -0.1348817 0.0401603 0.16781088 0.11805596 -0.16012405 0.05298418 -0.00717615 0.00035245] plot:
E, E_D, E_W= 8.75095182167 8.68552503195 0.0654267897187 Err = [0.7, 8.7509518216724125] **************************************************************** Lambda = 0.8 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [-0.11634705 -0.26847661 -0.12632283 0.03805302 0.16175572 0.11588564 -0.15634822 0.05169725 -0.00699841 0.00034355] plot:
E, E_D, E_W= 8.75651277825 8.68859970647 0.0679130717789 Err = [0.8, 8.756512778247453] **************************************************************** Lambda = 0.9 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [-0.11818457 -0.25092778 -0.11859948 0.03673508 0.15645347 0.11341197 -0.15266869 0.05047135 -0.00683067 0.00033519] plot:
E, E_D, E_W= 8.76151345843 8.6914981981 0.0700152603295 Err = [0.9, 8.761513458428162] **************************************************************** min_e = [0.3, 8.7275410196085232]
In [20]:
plt.plot(L, np.log(E_T) , 'b-')
plt.title('$Lambda \ vs. Error, blue:E(w)=E_D+E_W$')
plt.xlabel('lambda')
plt.ylabel('log( E(w) )')
Out[20]:
<matplotlib.text.Text at 0xafa4128>
In [21]:
Err = pd.DataFrame({'lambda':L,
'log(E)':np.log(E_T)})
Err
Out[21]:
| lambda | log(E) | |
|---|---|---|
| 0 | 0.0 | 2.580974 |
| 1 | 0.1 | 2.169336 |
| 2 | 0.2 | 2.166624 |
| 3 | 0.3 | 2.166484 |
| 4 | 0.4 | 2.167020 |
| 5 | 0.5 | 2.167729 |
| 6 | 0.6 | 2.168467 |
| 7 | 0.7 | 2.169162 |
| 8 | 0.8 | 2.169798 |
| 9 | 0.9 | 2.170369 |
In [22]:
%matplotlib inline
degree = 9
L = [] #lenda
E_T = [] #Error for sum
min_e = [0,350000] # i,j,e_t,e_d,e_w
for j in range(200,400): #設定lenda範圍
l = (float)(j)/1000
print 'Lambda =', l
print ''
e = Reg_L2(train_x, train_y,test_x, test_y, degree, l, False)
L.append( l )
E_T.append( e )
print 'Err =', [l,e]
if min_e[1] > e :
min_e = [l,e]
print ''
print "*" *64
print ''
print "min_e = " , min_e # 算出[0.3, 8.7275293447745863] 與原本答案[0.3, 8.7275410196083474]很接近
Lambda = 0.2 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.53750764e-02 -5.26785145e-01 -1.88927068e-01 1.06154491e-01 2.21757080e-01 9.12059584e-02 -1.64549003e-01 5.68589988e-02 -7.84679411e-03 3.89763503e-04] E, E_D, E_W= 8.72876581224 8.6871053646 0.0416604476406 Err = [0.2, 8.7287658122385192] **************************************************************** Lambda = 0.201 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.56116086e-02 -5.25684881e-01 -1.88849669e-01 1.05698851e-01 2.21575447e-01 9.14985286e-02 -1.64658343e-01 5.68775813e-02 -7.84828331e-03 3.89808200e-04] E, E_D, E_W= 8.72872368577 8.68697972862 0.0417439571539 Err = [0.201, 8.7287236857697117] **************************************************************** Lambda = 0.202 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.58347077e-02 -5.24591265e-01 -1.88772285e-01 1.05247356e-01 2.21394302e-01 9.17879579e-02 -1.64765958e-01 5.68957525e-02 -7.84972593e-03 3.89850850e-04] E, E_D, E_W= 8.72861976507 8.6867926987 0.0418270663711 Err = [0.202, 8.7286197650744715] **************************************************************** Lambda = 0.203 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.60726413e-02 -5.23496134e-01 -1.88695171e-01 1.04799793e-01 2.21213749e-01 9.20743814e-02 -1.64871959e-01 5.69135438e-02 -7.85112595e-03 3.89891641e-04] E, E_D, E_W= 8.72855798462 8.68664867708 0.0419093075479 Err = [0.203, 8.7285579846249757] **************************************************************** Lambda = 0.204 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.63181940e-02 -5.22405919e-01 -1.88617443e-01 1.04355814e-01 2.21033272e-01 9.23586881e-02 -1.64976804e-01 5.69310644e-02 -7.85249589e-03 3.89931133e-04] E, E_D, E_W= 8.72853868034 8.68654743629 0.0419912440573 Err = [0.204, 8.7285386803425613] **************************************************************** Lambda = 0.205 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.65427615e-02 -5.21329568e-01 -1.88537292e-01 1.03915181e-01 2.20853392e-01 9.26401560e-02 -1.65080127e-01 5.69482275e-02 -7.85382579e-03 3.89968881e-04] E, E_D, E_W= 8.72845638863 8.68638301521 0.0420733734159 Err = [0.205, 8.728456388630093] **************************************************************** Lambda = 0.206 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.67779583e-02 -5.20257862e-01 -1.88455903e-01 1.03478194e-01 2.20673692e-01 9.29192324e-02 -1.65182142e-01 5.69650821e-02 -7.85512118e-03 3.90005127e-04] E, E_D, E_W= 8.72842214679 8.68626694741 0.0421551993799 Err = [0.206, 8.7284221467876701] **************************************************************** Lambda = 0.207 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.70107682e-02 -5.19184726e-01 -1.88377220e-01 1.03045423e-01 2.20494625e-01 9.31952848e-02 -1.65282533e-01 5.69815536e-02 -7.85637349e-03 3.90039490e-04] E, E_D, E_W= 8.72835448474 8.68611842342 0.0422360613159 Err = [0.207, 8.7283544847381798] **************************************************************** Lambda = 0.208 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.72098643e-02 -5.18131902e-01 -1.88294373e-01 1.02615895e-01 2.20316282e-01 9.34682865e-02 -1.65381293e-01 5.69976408e-02 -7.85758264e-03 3.90071966e-04] E, E_D, E_W= 8.72818774291 8.68587016291 0.0423175800019 Err = [0.208, 8.7281877429081245] **************************************************************** Lambda = 0.209 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.74322536e-02 -5.17084999e-01 -1.88209282e-01 1.02189288e-01 2.20137735e-01 9.37398080e-02 -1.65479231e-01 5.70135379e-02 -7.85877099e-03 3.90103558e-04] E, E_D, E_W= 8.72814530996 8.68574621198 0.0423990979798 Err = [0.209, 8.7281453099621675] **************************************************************** Lambda = 0.21 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.76698490e-02 -5.16038122e-01 -1.88123872e-01 1.01766183e-01 2.19959859e-01 9.40083946e-02 -1.65575598e-01 5.70290649e-02 -7.85991776e-03 3.90133333e-04] E, E_D, E_W= 8.72815570229 8.68567575349 0.0424799487971 Err = [0.21, 8.7281557022910885] **************************************************************** Lambda = 0.211 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.78894861e-02 -5.14996647e-01 -1.88039308e-01 1.01346837e-01 2.19782483e-01 9.42743717e-02 -1.65670581e-01 5.70442689e-02 -7.86102856e-03 3.90161547e-04] E, E_D, E_W= 8.72808231346 8.68552200704 0.0425603064173 Err = [0.211, 8.7280823134604564] **************************************************************** Lambda = 0.212 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.81132440e-02 -5.13964509e-01 -1.87952881e-01 1.00931009e-01 2.19605401e-01 9.45378717e-02 -1.65764210e-01 5.70591524e-02 -7.86210333e-03 3.90188188e-04] E, E_D, E_W= 8.72805404037 8.68541323895 0.0426408014196 Err = [0.212, 8.7280540403692353] **************************************************************** Lambda = 0.213 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.83150802e-02 -5.12938156e-01 -1.87866526e-01 1.00518516e-01 2.19429097e-01 9.47984504e-02 -1.65856292e-01 5.70736733e-02 -7.86313748e-03 3.90213058e-04] E, E_D, E_W= 8.72791649716 8.68519573941 0.0427207577529 Err = [0.213, 8.7279164971634913] **************************************************************** Lambda = 0.214 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.85377987e-02 -5.11921728e-01 -1.87776813e-01 1.00108641e-01 2.19252475e-01 9.50578979e-02 -1.65947752e-01 5.70880535e-02 -7.86415664e-03 3.90237305e-04] E, E_D, E_W= 8.72791336117 8.68511223684 0.042801124334 Err = [0.214, 8.7279133611725168] **************************************************************** Lambda = 0.215 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.87597756e-02 -5.10891490e-01 -1.87692969e-01 9.97032888e-02 2.19076985e-01 9.53136147e-02 -1.66037230e-01 5.71019646e-02 -7.86512273e-03 3.90259218e-04] E, E_D, E_W= 8.72782272565 8.68494335824 0.0428793674078 Err = [0.215, 8.7278227256501673] **************************************************************** Lambda = 0.216 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.89613464e-02 -5.09890447e-01 -1.87601946e-01 9.93002208e-02 2.18901543e-01 9.55676614e-02 -1.66125805e-01 5.71156667e-02 -7.86606587e-03 3.90280149e-04] E, E_D, E_W= 8.72774808062 8.68478870109 0.04295937953 Err = [0.216, 8.7277480806184844] **************************************************************** Lambda = 0.217 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.92103699e-02 -5.08873149e-01 -1.87514789e-01 9.89012388e-02 2.18726952e-01 9.58185416e-02 -1.66212702e-01 5.71289739e-02 -7.86696459e-03 3.90299136e-04] E, E_D, E_W= 8.72782149033 8.68478384733 0.0430376430005 Err = [0.217, 8.727821490334339] **************************************************************** Lambda = 0.218 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.93917068e-02 -5.07883834e-01 -1.87422691e-01 9.85044021e-02 2.18552341e-01 9.60679847e-02 -1.66298823e-01 5.71421031e-02 -7.86784396e-03 3.90317302e-04] E, E_D, E_W= 8.72765288309 8.68453593425 0.0431169488395 Err = [0.218, 8.7276528830852538] **************************************************************** Lambda = 0.219 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.96214126e-02 -5.06884342e-01 -1.87332234e-01 9.81110881e-02 2.18378051e-01 9.63151361e-02 -1.66383709e-01 5.71549426e-02 -7.86869096e-03 3.90334062e-04] E, E_D, E_W= 8.72766554347 8.68447043219 0.0431951112807 Err = [0.219, 8.7276655434681985] **************************************************************** Lambda = 0.22 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -6.98490295e-02 -5.05892542e-01 -1.87240445e-01 9.77210162e-02 2.18204695e-01 9.65592156e-02 -1.66466981e-01 5.71674039e-02 -7.86949554e-03 3.90348969e-04] E, E_D, E_W= 8.72768468051 8.68441145571 0.0432732247965 Err = [0.22, 8.7276846805085384] **************************************************************** Lambda = 0.221 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.00402919e-02 -5.04915149e-01 -1.87146985e-01 9.73335135e-02 2.18031455e-01 9.68016303e-02 -1.66549356e-01 5.71796571e-02 -7.87027720e-03 3.90362893e-04] E, E_D, E_W= 8.72756714557 8.68421563949 0.0433515060886 Err = [0.221, 8.7275671455745734] **************************************************************** Lambda = 0.222 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.02440479e-02 -5.03941201e-01 -1.87053539e-01 9.69495981e-02 2.17858806e-01 9.70414376e-02 -1.66630357e-01 5.71915902e-02 -7.87102316e-03 3.90375265e-04] E, E_D, E_W= 8.72751278373 8.68408321559 0.0434295681443 Err = [0.222, 8.7275127837299458] **************************************************************** Lambda = 0.223 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.04642089e-02 -5.02968168e-01 -1.86959687e-01 9.65684979e-02 2.17686124e-01 9.72796726e-02 -1.66710493e-01 5.72033211e-02 -7.87174675e-03 3.90386675e-04] E, E_D, E_W= 8.72753225551 8.68402507922 0.0435071762986 Err = [0.223, 8.7275322555146975] **************************************************************** Lambda = 0.224 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.06683496e-02 -5.01997686e-01 -1.86866562e-01 9.61906068e-02 2.17514261e-01 9.75151507e-02 -1.66789192e-01 5.72147173e-02 -7.87243302e-03 3.90396462e-04] E, E_D, E_W= 8.72745821488 8.68387415613 0.043584058749 Err = [0.224, 8.7274582148830468] **************************************************************** Lambda = 0.225 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.08529009e-02 -5.01047402e-01 -1.86769521e-01 9.58154742e-02 2.17343030e-01 9.77481142e-02 -1.66866561e-01 5.72258028e-02 -7.87308455e-03 3.90404737e-04] E, E_D, E_W= 8.72736241861 8.68370047588 0.0436619427281 Err = [0.225, 8.72736241860596] **************************************************************** Lambda = 0.226 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.10862438e-02 -5.00082311e-01 -1.86674119e-01 9.54427107e-02 2.17171634e-01 9.79799986e-02 -1.66943341e-01 5.72367542e-02 -7.87372169e-03 3.90412410e-04] E, E_D, E_W= 8.72742117344 8.68368294355 0.0437382298929 Err = [0.226, 8.7274211734386746] **************************************************************** Lambda = 0.227 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.12997394e-02 -4.99131427e-01 -1.86577750e-01 9.50735864e-02 2.17001247e-01 9.82087058e-02 -1.67018466e-01 5.72473186e-02 -7.87431545e-03 3.90418188e-04] E, E_D, E_W= 8.72743475963 8.68361977017 0.043814989459 Err = [0.227, 8.7274347596311834] **************************************************************** Lambda = 0.228 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.14911915e-02 -4.98182359e-01 -1.86483097e-01 9.47074449e-02 2.16830990e-01 9.84357327e-02 -1.67092689e-01 5.72576739e-02 -7.87488613e-03 3.90422973e-04] E, E_D, E_W= 8.72732832409 8.68343741838 0.043890905709 Err = [0.228, 8.7273283240921593] **************************************************************** Lambda = 0.229 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.16929174e-02 -4.97246019e-01 -1.86384271e-01 9.43436554e-02 2.16661229e-01 9.86605802e-02 -1.67165759e-01 5.72677609e-02 -7.87542696e-03 3.90426466e-04] E, E_D, E_W= 8.72731459887 8.68334711873 0.0439674801328 Err = [0.229, 8.7273145988663963] **************************************************************** Lambda = 0.23 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.18804230e-02 -4.96308615e-01 -1.86287934e-01 9.39832430e-02 2.16492100e-01 9.88829580e-02 -1.67237535e-01 5.72775461e-02 -7.87593414e-03 3.90428495e-04] E, E_D, E_W= 8.72720468117 8.68316163127 0.0440430499049 Err = [0.23, 8.7272046811699653] **************************************************************** Lambda = 0.231 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.20809669e-02 -4.95378000e-01 -1.86188873e-01 9.36248334e-02 2.16322997e-01 9.91040640e-02 -1.67308634e-01 5.72871775e-02 -7.87642469e-03 3.90429824e-04] E, E_D, E_W= 8.72717346139 8.68305472762 0.0441187337723 Err = [0.231, 8.7271734613906897] **************************************************************** Lambda = 0.232 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.23016051e-02 -4.94451949e-01 -1.86089058e-01 9.32696870e-02 2.16154852e-01 9.93221352e-02 -1.67378137e-01 5.72964335e-02 -7.87687299e-03 3.90429301e-04] E, E_D, E_W= 8.72725660309 8.68306211694 0.0441944861554 Err = [0.232, 8.7272566030932612] **************************************************************** Lambda = 0.233 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.24862849e-02 -4.93530671e-01 -1.85989441e-01 9.29168430e-02 2.15986787e-01 9.95389067e-02 -1.67446965e-01 5.73055380e-02 -7.87730508e-03 3.90428099e-04] E, E_D, E_W= 8.72715203691 8.6828825847 0.0442694522084 Err = [0.233, 8.7271520369079756] **************************************************************** Lambda = 0.234 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.26866083e-02 -4.92617734e-01 -1.85889431e-01 9.25673004e-02 2.15819295e-01 9.97532418e-02 -1.67514502e-01 5.73143396e-02 -7.87770320e-03 3.90425415e-04] E, E_D, E_W= 8.72716087217 8.68281598964 0.0443448825273 Err = [0.234, 8.7271608721706251] **************************************************************** Lambda = 0.235 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.28778341e-02 -4.91710692e-01 -1.85786488e-01 9.22191686e-02 2.15651694e-01 9.99667467e-02 -1.67581606e-01 5.73230476e-02 -7.87809174e-03 3.90422349e-04] E, E_D, E_W= 8.72712284562 8.68270287255 0.0444199730632 Err = [0.235, 8.7271228456151277] **************************************************************** Lambda = 0.236 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.30757180e-02 -4.90803462e-01 -1.85686375e-01 9.18750228e-02 2.15485196e-01 1.00176958e-01 -1.67646990e-01 5.73313514e-02 -7.87843476e-03 3.90417285e-04] E, E_D, E_W= 8.72711267074 8.68261812044 0.0444945503085 Err = [0.236, 8.727112670744134] **************************************************************** Lambda = 0.237 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.32684400e-02 -4.89903113e-01 -1.85584151e-01 9.15329113e-02 2.15318891e-01 1.00385680e-01 -1.67711589e-01 5.73394749e-02 -7.87875805e-03 3.90411380e-04] E, E_D, E_W= 8.7270883009 8.68251928903 0.0445690118765 Err = [0.237, 8.7270883009039135] **************************************************************** Lambda = 0.238 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.34475287e-02 -4.89006929e-01 -1.85482616e-01 9.11935938e-02 2.15152831e-01 1.00592715e-01 -1.67775305e-01 5.73473961e-02 -7.87905918e-03 3.90404526e-04] E, E_D, E_W= 8.72699418937 8.68235115701 0.0446430323584 Err = [0.238, 8.7269941893688472] **************************************************************** Lambda = 0.239 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.36545255e-02 -4.88110417e-01 -1.85380927e-01 9.08570206e-02 2.14987721e-01 1.00797012e-01 -1.67837593e-01 5.73549833e-02 -7.87932283e-03 3.90396034e-04] E, E_D, E_W= 8.72702943509 8.68231266651 0.0447167685808 Err = [0.239, 8.7270294350880455] **************************************************************** Lambda = 0.24 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.38501791e-02 -4.87222771e-01 -1.85277109e-01 9.05222286e-02 2.14822421e-01 1.01000555e-01 -1.67899487e-01 5.73624854e-02 -7.87957781e-03 3.90387198e-04] E, E_D, E_W= 8.72702933416 8.68223879707 0.0447905370838 Err = [0.24, 8.7270293341568337] **************************************************************** Lambda = 0.241 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.40527985e-02 -4.86338199e-01 -1.85172614e-01 9.01900161e-02 2.14657689e-01 1.01201956e-01 -1.67960264e-01 5.73697284e-02 -7.87980402e-03 3.90377116e-04] E, E_D, E_W= 8.72706154839 8.68219750217 0.0448640462179 Err = [0.241, 8.7270615483859437] **************************************************************** Lambda = 0.242 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.42319301e-02 -4.85464909e-01 -1.85068458e-01 8.98609855e-02 2.14493752e-01 1.01400768e-01 -1.68019689e-01 5.73766554e-02 -7.87999485e-03 3.90365489e-04] E, E_D, E_W= 8.72702164549 8.68208387698 0.0449377685023 Err = [0.242, 8.7270216454850473] **************************************************************** Lambda = 0.243 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.44044565e-02 -4.84598053e-01 -1.84962880e-01 8.95340121e-02 2.14329877e-01 1.01598422e-01 -1.68078520e-01 5.73834504e-02 -7.88017164e-03 3.90353279e-04] E, E_D, E_W= 8.72695731509 8.68194594674 0.0450113683554 Err = [0.243, 8.7269573150924415] **************************************************************** Lambda = 0.244 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.46158118e-02 -4.83716707e-01 -1.84860016e-01 8.92095439e-02 2.14166442e-01 1.01794224e-01 -1.68136377e-01 5.73900192e-02 -7.88032333e-03 3.90339984e-04] E, E_D, E_W= 8.72700542049 8.68192203134 0.0450833891466 Err = [0.244, 8.7270054204907979] **************************************************************** Lambda = 0.245 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.47841244e-02 -4.82858923e-01 -1.84753020e-01 8.88874108e-02 2.14003896e-01 1.01987483e-01 -1.68192922e-01 5.73962829e-02 -7.88044099e-03 3.90325206e-04] E, E_D, E_W= 8.72692824905 8.68177173367 0.0451565153745 Err = [0.245, 8.7269282490487594] **************************************************************** Lambda = 0.246 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.49647897e-02 -4.82001116e-01 -1.84646760e-01 8.85674900e-02 2.13840985e-01 1.02180184e-01 -1.68249165e-01 5.74024829e-02 -7.88055234e-03 3.90310188e-04] E, E_D, E_W= 8.72690075658 8.68167153349 0.0452292230812 Err = [0.246, 8.7269007565750432] **************************************************************** Lambda = 0.247 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.51580657e-02 -4.81140987e-01 -1.84541592e-01 8.82505118e-02 2.13679162e-01 1.02370007e-01 -1.68303924e-01 5.74083363e-02 -7.88062485e-03 3.90293471e-04] E, E_D, E_W= 8.72691444127 8.68161319353 0.0453012477408 Err = [0.247, 8.726914441273939] **************************************************************** Lambda = 0.248 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.53405889e-02 -4.80286849e-01 -1.84436647e-01 8.79356774e-02 2.13517236e-01 1.02559005e-01 -1.68358261e-01 5.74140990e-02 -7.88068806e-03 3.90276382e-04] E, E_D, E_W= 8.72689047 8.68151734211 0.045373127894 Err = [0.248, 8.7268904699992671] **************************************************************** Lambda = 0.249 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.55424912e-02 -4.79434870e-01 -1.84328983e-01 8.76228346e-02 2.13356188e-01 1.02745501e-01 -1.68411293e-01 5.74195559e-02 -7.88071697e-03 3.90257794e-04] E, E_D, E_W= 8.72694732084 8.68150251444 0.0454448064057 Err = [0.249, 8.7269473208437063] **************************************************************** Lambda = 0.25 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.57258292e-02 -4.78591856e-01 -1.84221296e-01 8.73122865e-02 2.13195252e-01 1.02930794e-01 -1.68463703e-01 5.74248731e-02 -7.88073084e-03 3.90238573e-04] E, E_D, E_W= 8.72694789506 8.68143135197 0.0455165430915 Err = [0.25, 8.7269478950580375] **************************************************************** Lambda = 0.251 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.58823507e-02 -4.77758272e-01 -1.84113537e-01 8.70044080e-02 2.13035173e-01 1.03113647e-01 -1.68514860e-01 5.74298995e-02 -7.88071234e-03 3.90217946e-04] E, E_D, E_W= 8.72684237781 8.68125415415 0.0455882236672 Err = [0.251, 8.7268423778143465] **************************************************************** Lambda = 0.252 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.60599936e-02 -4.76932284e-01 -1.84002399e-01 8.66980827e-02 2.12875037e-01 1.03295606e-01 -1.68565547e-01 5.74348218e-02 -7.88068283e-03 3.90196863e-04] E, E_D, E_W= 8.72686046801 8.68120007266 0.0456603953532 Err = [0.252, 8.726860468014511] **************************************************************** Lambda = 0.253 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.62498942e-02 -4.76092443e-01 -1.83896079e-01 8.63945894e-02 2.12715498e-01 1.03475680e-01 -1.68615285e-01 5.74395283e-02 -7.88062973e-03 3.90174771e-04] E, E_D, E_W= 8.72686435144 8.68113363714 0.0457307142955 Err = [0.253, 8.7268643514391506] **************************************************************** Lambda = 0.254 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.64467697e-02 -4.75258561e-01 -1.83788728e-01 8.60933601e-02 2.12556233e-01 1.03654135e-01 -1.68664158e-01 5.74440326e-02 -7.88055409e-03 3.90151702e-04] E, E_D, E_W= 8.72692136343 8.68112018835 0.0458011750797 Err = [0.254, 8.7269213634315523] **************************************************************** Lambda = 0.255 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.66107336e-02 -4.74447362e-01 -1.83676457e-01 8.57939546e-02 2.12397744e-01 1.03830440e-01 -1.68711936e-01 5.74482854e-02 -7.88045063e-03 3.90127429e-04] E, E_D, E_W= 8.72689805872 8.68102521802 0.0458728407002 Err = [0.255, 8.7268980587157241] **************************************************************** Lambda = 0.256 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.68060788e-02 -4.73617309e-01 -1.83568691e-01 8.54966234e-02 2.12239224e-01 1.04005963e-01 -1.68759319e-01 5.74524533e-02 -7.88033848e-03 3.90102810e-04] E, E_D, E_W= 8.72692815984 8.68098574216 0.0459424176745 Err = [0.256, 8.7269281598382911] **************************************************************** Lambda = 0.257 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.69897354e-02 -4.72801024e-01 -1.83459146e-01 8.52017684e-02 2.12081289e-01 1.04179490e-01 -1.68805674e-01 5.74563836e-02 -7.88019999e-03 3.90077050e-04] E, E_D, E_W= 8.72696009525 8.68094739257 0.0460127026807 Err = [0.257, 8.7269600952467474] **************************************************************** Lambda = 0.258 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.71355409e-02 -4.72001027e-01 -1.83347109e-01 8.49088425e-02 2.11923797e-01 1.04351313e-01 -1.68851146e-01 5.74601104e-02 -7.88003905e-03 3.90050322e-04] E, E_D, E_W= 8.72685724528 8.680773795 0.0460834502746 Err = [0.258, 8.7268572452769018] **************************************************************** Lambda = 0.259 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.73176742e-02 -4.71190704e-01 -1.83237997e-01 8.46184019e-02 2.11766728e-01 1.04521623e-01 -1.68895858e-01 5.74636665e-02 -7.87985958e-03 3.90022808e-04] E, E_D, E_W= 8.72688403701 8.68073086055 0.0461531764608 Err = [0.259, 8.7268840370146812] **************************************************************** Lambda = 0.26 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.74885082e-02 -4.70389238e-01 -1.83126975e-01 8.43298028e-02 2.11609939e-01 1.04690443e-01 -1.68939782e-01 5.74670397e-02 -7.87965989e-03 3.89994421e-04] E, E_D, E_W= 8.7268789226 8.68065589956 0.046223023031 Err = [0.26, 8.7268789225954251] **************************************************************** Lambda = 0.261 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.76698417e-02 -4.69587797e-01 -1.83015216e-01 8.40428783e-02 2.11453643e-01 1.04857831e-01 -1.68983010e-01 5.74702596e-02 -7.87944388e-03 3.89965353e-04] E, E_D, E_W= 8.72690304985 8.68061062668 0.0462924231774 Err = [0.261, 8.7269030498529592] **************************************************************** Lambda = 0.262 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.78371841e-02 -4.68792747e-01 -1.82903749e-01 8.37583482e-02 2.11297842e-01 1.05023363e-01 -1.69025261e-01 5.74732511e-02 -7.87920232e-03 3.89935171e-04] E, E_D, E_W= 8.72688364609 8.68052194159 0.0463617044996 Err = [0.262, 8.7268836460933255] **************************************************************** Lambda = 0.263 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.80064230e-02 -4.68002240e-01 -1.82792083e-01 8.34761683e-02 2.11142673e-01 1.05186922e-01 -1.69066501e-01 5.74760100e-02 -7.87893501e-03 3.89903876e-04] E, E_D, E_W= 8.72688232628 8.68045138116 0.0464309451204 Err = [0.263, 8.7268823262832367] **************************************************************** Lambda = 0.264 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.81893191e-02 -4.67211487e-01 -1.82680596e-01 8.31958122e-02 2.10987555e-01 1.05349634e-01 -1.69107325e-01 5.74786794e-02 -7.87865853e-03 3.89872212e-04] E, E_D, E_W= 8.72693680212 8.68043696519 0.0464998369296 Err = [0.264, 8.7269368021235483] **************************************************************** Lambda = 0.265 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.83432418e-02 -4.66431191e-01 -1.82568193e-01 8.29174385e-02 2.10832989e-01 1.05510576e-01 -1.69147237e-01 5.74811388e-02 -7.87835881e-03 3.89839544e-04] E, E_D, E_W= 8.72687837503 8.68030962211 0.0465687529208 Err = [0.265, 8.7268783750307044] **************************************************************** Lambda = 0.266 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.85246615e-02 -4.65649633e-01 -1.82455399e-01 8.26406821e-02 2.10678673e-01 1.05670483e-01 -1.69186648e-01 5.74834904e-02 -7.87804790e-03 3.89806421e-04] E, E_D, E_W= 8.72694221845 8.68030475747 0.0466374609795 Err = [0.266, 8.7269422184453127] **************************************************************** Lambda = 0.267 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.86864546e-02 -4.64874621e-01 -1.82342004e-01 8.23659926e-02 2.10524812e-01 1.05828686e-01 -1.69225170e-01 5.74856354e-02 -7.87771400e-03 3.89772299e-04] E, E_D, E_W= 8.72692299362 8.68021706971 0.046705923914 Err = [0.267, 8.7269229936239228] **************************************************************** Lambda = 0.268 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.88457873e-02 -4.64101290e-01 -1.82229206e-01 8.20934946e-02 2.10371587e-01 1.05984994e-01 -1.69262722e-01 5.74875570e-02 -7.87735538e-03 3.89737108e-04] E, E_D, E_W= 8.72688554069 8.6801115801 0.046773960592 Err = [0.268, 8.7268855406920913] **************************************************************** Lambda = 0.269 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.90138298e-02 -4.63331216e-01 -1.82116329e-01 8.18227696e-02 2.10218451e-01 1.06140481e-01 -1.69299879e-01 5.74893950e-02 -7.87698828e-03 3.89701580e-04] E, E_D, E_W= 8.72690021956 8.6800582272 0.0468419923638 Err = [0.269, 8.7269002195601502] **************************************************************** Lambda = 0.27 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.91976805e-02 -4.62565310e-01 -1.82001251e-01 8.15537245e-02 2.10065582e-01 1.06294647e-01 -1.69336352e-01 5.74910772e-02 -7.87660415e-03 3.89665325e-04] E, E_D, E_W= 8.72699815666 8.68008798921 0.0469101674523 Err = [0.27, 8.7269981566642407] **************************************************************** Lambda = 0.271 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.93645784e-02 -4.61793841e-01 -1.81890441e-01 8.12872746e-02 2.09913437e-01 1.06446823e-01 -1.69371812e-01 5.74925256e-02 -7.87619406e-03 3.89627944e-04] E, E_D, E_W= 8.72697343198 8.67999652968 0.0469769023012 Err = [0.271, 8.7269734319817296] **************************************************************** Lambda = 0.272 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.95199445e-02 -4.61040875e-01 -1.81775081e-01 8.10223010e-02 2.09761893e-01 1.06597271e-01 -1.69406392e-01 5.74937721e-02 -7.87576170e-03 3.89589603e-04] E, E_D, E_W= 8.72696458463 8.67991971327 0.0470448713634 Err = [0.272, 8.7269645846334694] **************************************************************** Lambda = 0.273 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.96919792e-02 -4.60283376e-01 -1.81661099e-01 8.07591211e-02 2.09610368e-01 1.06747012e-01 -1.69440631e-01 5.74949477e-02 -7.87532229e-03 3.89550987e-04] E, E_D, E_W= 8.7270094474 8.67989732033 0.0471121270785 Err = [0.273, 8.7270094474040878] **************************************************************** Lambda = 0.274 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.98458413e-02 -4.59538012e-01 -1.81544031e-01 8.04969648e-02 2.09459037e-01 1.06895848e-01 -1.69474423e-01 5.74960257e-02 -7.87487267e-03 3.89511952e-04] E, E_D, E_W= 8.72700001902 8.67982024836 0.0471797706656 Err = [0.274, 8.7270000190248354] **************************************************************** Lambda = 0.275 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -7.99958841e-02 -4.58789590e-01 -1.81430174e-01 8.02376562e-02 2.09308412e-01 1.07042636e-01 -1.69507172e-01 5.74968636e-02 -7.87439639e-03 3.89471761e-04] E, E_D, E_W= 8.72695490639 8.67970838225 0.0472465241434 Err = [0.275, 8.7269549063948872] **************************************************************** Lambda = 0.276 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.01901581e-02 -4.58033046e-01 -1.81316866e-01 7.99802116e-02 2.09158165e-01 1.07187972e-01 -1.69539171e-01 5.74975288e-02 -7.87390107e-03 3.89430751e-04] E, E_D, E_W= 8.72708076333 8.67976809551 0.0473126678244 Err = [0.276, 8.7270807633320775] **************************************************************** Lambda = 0.277 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.03240934e-02 -4.57304325e-01 -1.81199865e-01 7.97241733e-02 2.09008468e-01 1.07331864e-01 -1.69570455e-01 5.74980334e-02 -7.87338832e-03 3.89388999e-04] E, E_D, E_W= 8.7270221302 8.67964190143 0.0473802287752 Err = [0.277, 8.7270221302012008] **************************************************************** Lambda = 0.278 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.04868710e-02 -4.56561880e-01 -1.81085241e-01 7.94699899e-02 2.08858904e-01 1.07474823e-01 -1.69601276e-01 5.74984370e-02 -7.87286499e-03 3.89346812e-04] E, E_D, E_W= 8.72703151299 8.67958526604 0.047446246949 Err = [0.278, 8.7270315129875069] **************************************************************** Lambda = 0.279 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.06608262e-02 -4.55821512e-01 -1.80970210e-01 7.92174395e-02 2.08709811e-01 1.07616368e-01 -1.69631386e-01 5.74986793e-02 -7.87232406e-03 3.89303873e-04] E, E_D, E_W= 8.72709452896 8.67958234427 0.0475121846875 Err = [0.279, 8.7270945289596344] **************************************************************** Lambda = 0.28 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.07907962e-02 -4.55100049e-01 -1.80853156e-01 7.89667569e-02 2.08560973e-01 1.07756720e-01 -1.69660896e-01 5.74987873e-02 -7.87176866e-03 3.89260323e-04] E, E_D, E_W= 8.72701677728 8.67943790943 0.0475788678459 Err = [0.28, 8.7270167772771767] **************************************************************** Lambda = 0.281 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.09654050e-02 -4.54369772e-01 -1.80736289e-01 7.87176596e-02 2.08412791e-01 1.07895374e-01 -1.69689547e-01 5.74986982e-02 -7.87119149e-03 3.89215834e-04] E, E_D, E_W= 8.72710490822 8.67946003537 0.0476448728546 Err = [0.281, 8.7271049082211416] **************************************************************** Lambda = 0.282 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.11247189e-02 -4.53641344e-01 -1.80620924e-01 7.84704038e-02 2.08264722e-01 1.08033116e-01 -1.69717735e-01 5.74985065e-02 -7.87060339e-03 3.89170889e-04] E, E_D, E_W= 8.72711825679 8.67940796751 0.0477102892774 Err = [0.282, 8.7271182567903729] **************************************************************** Lambda = 0.283 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.12708231e-02 -4.52924973e-01 -1.80502585e-01 7.82242556e-02 2.08117013e-01 1.08169858e-01 -1.69745455e-01 5.74982154e-02 -7.87000509e-03 3.89125532e-04] E, E_D, E_W= 8.72710661308 8.67933037516 0.0477762379187 Err = [0.283, 8.7271066130751862] **************************************************************** Lambda = 0.284 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.14397611e-02 -4.52199648e-01 -1.80387558e-01 7.79806117e-02 2.07970080e-01 1.08304513e-01 -1.69772102e-01 5.74976744e-02 -7.86937883e-03 3.89078953e-04] E, E_D, E_W= 8.72716246253 8.67932129182 0.047841170715 Err = [0.284, 8.7271624625311777] **************************************************************** Lambda = 0.285 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.16096543e-02 -4.51476409e-01 -1.80273084e-01 7.77387191e-02 2.07823032e-01 1.08438655e-01 -1.69798503e-01 5.74970844e-02 -7.86874792e-03 3.89032203e-04] E, E_D, E_W= 8.72722606889 8.67932018421 0.0479058846728 Err = [0.285, 8.727226068886127] **************************************************************** Lambda = 0.286 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.17531280e-02 -4.50771329e-01 -1.80154302e-01 7.74980713e-02 2.07676820e-01 1.08570857e-01 -1.69823924e-01 5.74962684e-02 -7.86809192e-03 3.88984366e-04] E, E_D, E_W= 8.72722382841 8.67925230118 0.04797152723 Err = [0.286, 8.7272238284055668] **************************************************************** Lambda = 0.287 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.18857902e-02 -4.50064717e-01 -1.80037179e-01 7.72588405e-02 2.07530710e-01 1.08702395e-01 -1.69849030e-01 5.74953871e-02 -7.86742942e-03 3.88936274e-04] E, E_D, E_W= 8.72714690517 8.67911063702 0.0480362681474 Err = [0.287, 8.7271469051688975] **************************************************************** Lambda = 0.288 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.20496170e-02 -4.49355943e-01 -1.79920463e-01 7.70217046e-02 2.07385132e-01 1.08832355e-01 -1.69873342e-01 5.74943247e-02 -7.86674701e-03 3.88887327e-04] E, E_D, E_W= 8.7272163136 8.67911537722 0.0481009363791 Err = [0.288, 8.7272163136036625] **************************************************************** Lambda = 0.289 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.22051933e-02 -4.48652287e-01 -1.79802280e-01 7.67857158e-02 2.07239810e-01 1.08961317e-01 -1.69897155e-01 5.74931515e-02 -7.86605278e-03 3.88837883e-04] E, E_D, E_W= 8.72724614274 8.67908069265 0.0481654500857 Err = [0.289, 8.7272461427363961] **************************************************************** Lambda = 0.29 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.23401664e-02 -4.47957481e-01 -1.79684194e-01 7.65514966e-02 2.07094767e-01 1.09089247e-01 -1.69920465e-01 5.74918683e-02 -7.86534692e-03 3.88787957e-04] E, E_D, E_W= 8.72721082199 8.67898072635 0.0482300956415 Err = [0.29, 8.7272108219889528] **************************************************************** Lambda = 0.291 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.25058393e-02 -4.47254727e-01 -1.79567541e-01 7.63190489e-02 2.06950391e-01 1.09215535e-01 -1.69942955e-01 5.74903979e-02 -7.86462045e-03 3.88737144e-04] E, E_D, E_W= 8.72728172981 8.67898775413 0.0482939756829 Err = [0.291, 8.7272817298131802] **************************************************************** Lambda = 0.292 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.26548138e-02 -4.46564688e-01 -1.79448578e-01 7.60879920e-02 2.06806040e-01 1.09341076e-01 -1.69965066e-01 5.74888446e-02 -7.86388528e-03 3.88685974e-04] E, E_D, E_W= 8.72731770426 8.67895924769 0.0483584565713 Err = [0.292, 8.7273177042569934] **************************************************************** Lambda = 0.293 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.28126401e-02 -4.45867201e-01 -1.79331661e-01 7.58584105e-02 2.06662198e-01 1.09465382e-01 -1.69986579e-01 5.74871589e-02 -7.86313595e-03 3.88634207e-04] E, E_D, E_W= 8.72734896522 8.67892720481 0.048421760411 Err = [0.293, 8.7273489652202425] **************************************************************** Lambda = 0.294 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.29349737e-02 -4.45188923e-01 -1.79212321e-01 7.56305060e-02 2.06518997e-01 1.09588025e-01 -1.70007253e-01 5.74852808e-02 -7.86236533e-03 3.88581520e-04] E, E_D, E_W= 8.72728622891 8.6788001841 0.0484860448042 Err = [0.294, 8.7272862289076283] **************************************************************** Lambda = 0.295 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.31128964e-02 -4.44494583e-01 -1.79095389e-01 7.54043869e-02 2.06375878e-01 1.09709875e-01 -1.70027534e-01 5.74833170e-02 -7.86158574e-03 3.88528465e-04] E, E_D, E_W= 8.7274210975 8.67887190376 0.0485491937382 Err = [0.295, 8.727421097501697] **************************************************************** Lambda = 0.296 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.32512117e-02 -4.43814252e-01 -1.78976046e-01 7.51789339e-02 2.06233073e-01 1.09830976e-01 -1.70047479e-01 5.74812856e-02 -7.86079956e-03 3.88475156e-04] E, E_D, E_W= 8.72740192297 8.67878936023 0.048612562741 Err = [0.296, 8.7274019229736712] **************************************************************** Lambda = 0.297 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.34147626e-02 -4.43129361e-01 -1.78858438e-01 7.49558749e-02 2.06090946e-01 1.09950138e-01 -1.70066426e-01 5.74790211e-02 -7.85998724e-03 3.88420705e-04] E, E_D, E_W= 8.72748419481 8.67880867985 0.0486755149584 Err = [0.297, 8.7274841948058626] **************************************************************** Lambda = 0.298 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.35397890e-02 -4.42463294e-01 -1.78737740e-01 7.47336292e-02 2.05948748e-01 1.10069029e-01 -1.70085267e-01 5.74767420e-02 -7.85917430e-03 3.88366264e-04] E, E_D, E_W= 8.72745429508 8.67871497555 0.0487393195299 Err = [0.298, 8.7274542950828877] **************************************************************** Lambda = 0.299 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.37002267e-02 -4.41781062e-01 -1.78620395e-01 7.45132396e-02 2.05807196e-01 1.10186315e-01 -1.70103301e-01 5.74742781e-02 -7.85834094e-03 3.88310940e-04] E, E_D, E_W= 8.72751310955 8.67871157654 0.0488015330091 Err = [0.299, 8.7275131095516798] **************************************************************** Lambda = 0.3 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.38434168e-02 -4.41112786e-01 -1.78500826e-01 7.42942548e-02 2.05666018e-01 1.10302458e-01 -1.70120775e-01 5.74716898e-02 -7.85749423e-03 3.88255055e-04] E, E_D, E_W= 8.72754101961 8.67867649504 0.0488645245643 Err = [0.3, 8.7275410196085232] **************************************************************** Lambda = 0.301 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.39973581e-02 -4.40438831e-01 -1.78383349e-01 7.40767901e-02 2.05524975e-01 1.10417951e-01 -1.70137951e-01 5.74690409e-02 -7.85664160e-03 3.88198941e-04] E, E_D, E_W= 8.72759305018 8.67866639727 0.0489266529174 Err = [0.301, 8.7275930501831773] **************************************************************** Lambda = 0.302 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.41401226e-02 -4.39775152e-01 -1.78264073e-01 7.38607955e-02 2.05384414e-01 1.10531990e-01 -1.70154378e-01 5.74662190e-02 -7.85576974e-03 3.88141994e-04] E, E_D, E_W= 8.72762400805 8.67863476659 0.0489892414629 Err = [0.302, 8.7276240080511638] **************************************************************** Lambda = 0.303 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.42784049e-02 -4.39115325e-01 -1.78144420e-01 7.36459338e-02 2.05244232e-01 1.10645165e-01 -1.70170418e-01 5.74633169e-02 -7.85488982e-03 3.88084727e-04] E, E_D, E_W= 8.72764133713 8.67858962507 0.0490517120633 Err = [0.303, 8.7276413371349744] **************************************************************** Lambda = 0.304 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.44255291e-02 -4.38455260e-01 -1.78024582e-01 7.34324970e-02 2.05104221e-01 1.10757458e-01 -1.70186017e-01 5.74603176e-02 -7.85399957e-03 3.88027030e-04] E, E_D, E_W= 8.7276895571 8.67857569267 0.0491138644339 Err = [0.304, 8.7276895571014244] **************************************************************** Lambda = 0.305 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.45644802e-02 -4.37796639e-01 -1.77905266e-01 7.32205675e-02 2.04964974e-01 1.10868023e-01 -1.70200742e-01 5.74571163e-02 -7.85308683e-03 3.87968354e-04] E, E_D, E_W= 8.72769196962 8.67851645715 0.0491755124691 Err = [0.305, 8.7276919696239972] **************************************************************** Lambda = 0.306 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.46783404e-02 -4.37151324e-01 -1.77785802e-01 7.30105380e-02 2.04825850e-01 1.10977813e-01 -1.70215106e-01 5.74538396e-02 -7.85216649e-03 3.87909376e-04] E, E_D, E_W= 8.72763445024 8.67839665302 0.0492377972146 Err = [0.306, 8.7276344502376233] **************************************************************** Lambda = 0.307 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.48485211e-02 -4.36489200e-01 -1.77667319e-01 7.28013912e-02 2.04687270e-01 1.11086402e-01 -1.70228875e-01 5.74504287e-02 -7.85123151e-03 3.87849774e-04] E, E_D, E_W= 8.72776209775 8.67846325591 0.0492988418458 Err = [0.307, 8.727762097754729] **************************************************************** Lambda = 0.308 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.49810639e-02 -4.35844314e-01 -1.77546327e-01 7.25933193e-02 2.04548602e-01 1.11194697e-01 -1.70242530e-01 5.74470013e-02 -7.85029569e-03 3.87790171e-04] E, E_D, E_W= 8.72777204113 8.67841144047 0.0493606006632 Err = [0.308, 8.7277720411285635] **************************************************************** Lambda = 0.309 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.51170021e-02 -4.35196625e-01 -1.77426871e-01 7.23867385e-02 2.04410589e-01 1.11301528e-01 -1.70255461e-01 5.74434098e-02 -7.84934187e-03 3.87729794e-04] E, E_D, E_W= 8.72777501875 8.67835335943 0.0494216593225 Err = [0.309, 8.7277750187514158] **************************************************************** Lambda = 0.31 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.52783113e-02 -4.34546894e-01 -1.77307968e-01 7.21820103e-02 2.04272856e-01 1.11407200e-01 -1.70267803e-01 5.74396841e-02 -7.84837336e-03 3.87668789e-04] E, E_D, E_W= 8.72789237056 8.67840972039 0.0494826501725 Err = [0.31, 8.7278923705600953] **************************************************************** Lambda = 0.311 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.54073618e-02 -4.33908133e-01 -1.77187635e-01 7.19781780e-02 2.04135540e-01 1.11511979e-01 -1.70279734e-01 5.74358717e-02 -7.84739594e-03 3.87607422e-04] E, E_D, E_W= 8.72788905932 8.67834535346 0.0495437058551 Err = [0.311, 8.7278890593167553] **************************************************************** Lambda = 0.312 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.55371969e-02 -4.33277535e-01 -1.77065110e-01 7.17753906e-02 2.03998447e-01 1.11615897e-01 -1.70291237e-01 5.74319642e-02 -7.84640835e-03 3.87545629e-04] E, E_D, E_W= 8.72791706223 8.67831178897 0.0496052732691 Err = [0.312, 8.7279170622347682] **************************************************************** Lambda = 0.313 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.56809790e-02 -4.32638126e-01 -1.76945734e-01 7.15742260e-02 2.03861642e-01 1.11718960e-01 -1.70302339e-01 5.74279712e-02 -7.84541192e-03 3.87483476e-04] E, E_D, E_W= 8.72796693827 8.67830121017 0.0496657280973 Err = [0.313, 8.727966938269919] **************************************************************** Lambda = 0.314 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.58185613e-02 -4.31999001e-01 -1.76827892e-01 7.13749115e-02 2.03725344e-01 1.11820627e-01 -1.70312741e-01 5.74238186e-02 -7.84439792e-03 3.87420567e-04] E, E_D, E_W= 8.72798126971 8.67825561408 0.0497256556308 Err = [0.314, 8.7279812697082928] **************************************************************** Lambda = 0.315 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.59431116e-02 -4.31378466e-01 -1.76704355e-01 7.11758308e-02 2.03589308e-01 1.11921662e-01 -1.70322860e-01 5.74196088e-02 -7.84337835e-03 3.87357443e-04] E, E_D, E_W= 8.72800232079 8.67821536609 0.0497869546988 Err = [0.315, 8.728002320787871] **************************************************************** Lambda = 0.316 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.60940097e-02 -4.30741136e-01 -1.76586267e-01 7.09790729e-02 2.03453940e-01 1.12021023e-01 -1.70332136e-01 5.74152051e-02 -7.84233724e-03 3.87293384e-04] E, E_D, E_W= 8.72807213341 8.6782256214 0.0498465120063 Err = [0.316, 8.7280721334108442] **************************************************************** Lambda = 0.317 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.62224888e-02 -4.30116528e-01 -1.76465790e-01 7.07829521e-02 2.03318403e-01 1.12120360e-01 -1.70341436e-01 5.74108172e-02 -7.84129893e-03 3.87229486e-04] E, E_D, E_W= 8.7280791921 8.67817259234 0.0499065997571 Err = [0.317, 8.7280791921019318] **************************************************************** Lambda = 0.318 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.63489874e-02 -4.29498904e-01 -1.76344585e-01 7.05882361e-02 2.03183473e-01 1.12218332e-01 -1.70350070e-01 5.74062798e-02 -7.84024434e-03 3.87164892e-04] E, E_D, E_W= 8.72810424606 8.67813717759 0.0499670684721 Err = [0.318, 8.7281042460606386] **************************************************************** Lambda = 0.319 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.64864146e-02 -4.28878389e-01 -1.76223104e-01 7.03946010e-02 2.03048812e-01 1.12315405e-01 -1.70358257e-01 5.74016426e-02 -7.83917903e-03 3.87099846e-04] E, E_D, E_W= 8.72815668539 8.67812973176 0.0500269536275 Err = [0.319, 8.7281566853898873] **************************************************************** Lambda = 0.32 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.66224310e-02 -4.28257296e-01 -1.76103546e-01 7.02024039e-02 2.02914414e-01 1.12411768e-01 -1.70366129e-01 5.73969414e-02 -7.83810740e-03 3.87034554e-04] E, E_D, E_W= 8.72819478461 8.67810840524 0.0500863793692 Err = [0.32, 8.7281947846098173] **************************************************************** Lambda = 0.321 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.67634231e-02 -4.27638930e-01 -1.75982759e-01 7.00113367e-02 2.02780602e-01 1.12506721e-01 -1.70373286e-01 5.73920755e-02 -7.83701749e-03 3.86968469e-04] E, E_D, E_W= 8.72826073309 8.67811496781 0.0501457652783 Err = [0.321, 8.7282607330880211] **************************************************************** Lambda = 0.322 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.68879751e-02 -4.27027819e-01 -1.75862392e-01 6.98216866e-02 2.02646772e-01 1.12601340e-01 -1.70380319e-01 5.73871917e-02 -7.83592661e-03 3.86902379e-04] E, E_D, E_W= 8.72827734052 8.67807206698 0.050205273542 Err = [0.322, 8.7282773405172112] **************************************************************** Lambda = 0.323 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.70179271e-02 -4.26418029e-01 -1.75740234e-01 6.96325683e-02 2.02513465e-01 1.12694875e-01 -1.70386825e-01 5.73821903e-02 -7.83482301e-03 3.86835750e-04] E, E_D, E_W= 8.72830326958 8.67803873022 0.0502645393591 Err = [0.323, 8.7283032695838507] **************************************************************** Lambda = 0.324 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.71660811e-02 -4.25806414e-01 -1.75619690e-01 6.94454001e-02 2.02380605e-01 1.12787113e-01 -1.70392680e-01 5.73770406e-02 -7.83370311e-03 3.86768419e-04] E, E_D, E_W= 8.72841696559 8.67809326688 0.0503236987134 Err = [0.324, 8.7284169655902684] **************************************************************** Lambda = 0.325 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.72906530e-02 -4.25198147e-01 -1.75499571e-01 6.92591767e-02 2.02247865e-01 1.12878911e-01 -1.70398354e-01 5.73718582e-02 -7.83258045e-03 3.86700999e-04] E, E_D, E_W= 8.728415441 8.67803320481 0.0503822361854 Err = [0.325, 8.7284154409981838] **************************************************************** Lambda = 0.326 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.74144115e-02 -4.24594279e-01 -1.75379294e-01 6.90740296e-02 2.02115440e-01 1.12969920e-01 -1.70403661e-01 5.73665978e-02 -7.83144974e-03 3.86633252e-04] E, E_D, E_W= 8.72842227434 8.67798137525 0.0504408990938 Err = [0.326, 8.7284222743408346] **************************************************************** Lambda = 0.327 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.75488270e-02 -4.23991230e-01 -1.75258609e-01 6.88903008e-02 2.01983423e-01 1.13059742e-01 -1.70408369e-01 5.73612008e-02 -7.83030398e-03 3.86564857e-04] E, E_D, E_W= 8.72848359374 8.67798410004 0.0504994937058 Err = [0.327, 8.7284835937415934] **************************************************************** Lambda = 0.328 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.76620710e-02 -4.23398284e-01 -1.75136652e-01 6.87075452e-02 2.01851912e-01 1.13148508e-01 -1.70412579e-01 5.73556952e-02 -7.82914670e-03 3.86495981e-04] E, E_D, E_W= 8.72847246917 8.67791411188 0.0505583572918 Err = [0.328, 8.7284724691741538] **************************************************************** Lambda = 0.329 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.78117269e-02 -4.22795200e-01 -1.75015839e-01 6.85258752e-02 2.01720455e-01 1.13236798e-01 -1.70416567e-01 5.73501440e-02 -7.82798497e-03 3.86426933e-04] E, E_D, E_W= 8.72858932167 8.67797283516 0.0506164865103 Err = [0.329, 8.7285893216741499] **************************************************************** Lambda = 0.33 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.79154027e-02 -4.22209874e-01 -1.74893587e-01 6.83452067e-02 2.01589199e-01 1.13324579e-01 -1.70420351e-01 5.73445566e-02 -7.82682018e-03 3.86357787e-04] E, E_D, E_W= 8.72855318097 8.67787793584 0.050675245129 Err = [0.33, 8.7285531809739254] **************************************************************** Lambda = 0.331 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.80618843e-02 -4.21610617e-01 -1.74773600e-01 6.81661092e-02 2.01458691e-01 1.13410653e-01 -1.70423264e-01 5.73387661e-02 -7.82563259e-03 3.86287643e-04] E, E_D, E_W= 8.72865808458 8.67792511372 0.0507329708616 Err = [0.331, 8.7286580845791324] **************************************************************** Lambda = 0.332 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.81758883e-02 -4.21024932e-01 -1.74651369e-01 6.79878814e-02 2.01328298e-01 1.13496210e-01 -1.70425946e-01 5.73329298e-02 -7.82444064e-03 3.86217337e-04] E, E_D, E_W= 8.72865278571 8.67786170843 0.0507910772819 Err = [0.332, 8.7286527857131873] **************************************************************** Lambda = 0.333 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.83262806e-02 -4.20424599e-01 -1.74532587e-01 6.78108532e-02 2.01198092e-01 1.13581257e-01 -1.70428412e-01 5.73270524e-02 -7.82324492e-03 3.86146894e-04] E, E_D, E_W= 8.72875550133 8.67790752147 0.0508479798656 Err = [0.333, 8.7287555013323157] **************************************************************** Lambda = 0.334 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.84355228e-02 -4.19847078e-01 -1.74409939e-01 6.76347353e-02 2.01068323e-01 1.13665229e-01 -1.70430348e-01 5.73210558e-02 -7.82203623e-03 3.86075899e-04] E, E_D, E_W= 8.72875314552 8.6778469547 0.0509061908252 Err = [0.334, 8.7287531455219192] **************************************************************** Lambda = 0.335 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.85632874e-02 -4.19263680e-01 -1.74288572e-01 6.74596868e-02 2.00938814e-01 1.13748604e-01 -1.70432031e-01 5.73150101e-02 -7.82082295e-03 3.86004735e-04] E, E_D, E_W= 8.72880857376 8.6778447962 0.0509637775587 Err = [0.335, 8.728808573755904] **************************************************************** Lambda = 0.336 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.86813245e-02 -4.18684208e-01 -1.74167375e-01 6.72860657e-02 2.00809823e-01 1.13830643e-01 -1.70433037e-01 5.73088082e-02 -7.81959229e-03 3.85932816e-04] E, E_D, E_W= 8.72882781146 8.67780662346 0.051021188006 Err = [0.336, 8.7288278114648623] **************************************************************** Lambda = 0.337 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.88096543e-02 -4.18102568e-01 -1.74046593e-01 6.71134914e-02 2.00681085e-01 1.13911969e-01 -1.70433710e-01 5.73025358e-02 -7.81835439e-03 3.85860602e-04] E, E_D, E_W= 8.72887705992 8.67779886463 0.0510781952953 Err = [0.337, 8.7288770599248728] **************************************************************** Lambda = 0.338 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.89348164e-02 -4.17528288e-01 -1.73924441e-01 6.69417368e-02 2.00552533e-01 1.13992784e-01 -1.70434163e-01 5.72962206e-02 -7.81711248e-03 3.85788242e-04] E, E_D, E_W= 8.72894108366 8.67780544044 0.0511356432203 Err = [0.338, 8.7289410836599384] **************************************************************** Lambda = 0.339 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.90642604e-02 -4.16949813e-01 -1.73803952e-01 6.67710537e-02 2.00424222e-01 1.14073012e-01 -1.70434361e-01 5.72898554e-02 -7.81586578e-03 3.85715702e-04] E, E_D, E_W= 8.72899655735 8.67780424652 0.0511923108261 Err = [0.339, 8.7289965573470774] **************************************************************** Lambda = 0.34 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.91934513e-02 -4.16372788e-01 -1.73684010e-01 6.66017523e-02 2.00296240e-01 1.14152263e-01 -1.70434076e-01 5.72833814e-02 -7.81460728e-03 3.85642659e-04] E, E_D, E_W= 8.72905315773 8.67780437997 0.0512487777549 Err = [0.34, 8.7290531577257156] **************************************************************** Lambda = 0.341 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.93148464e-02 -4.15802927e-01 -1.73562367e-01 6.64333903e-02 2.00168791e-01 1.14230312e-01 -1.70433195e-01 5.72767720e-02 -7.81333390e-03 3.85568976e-04] E, E_D, E_W= 8.72909545431 8.67778997274 0.0513054815653 Err = [0.341, 8.7290954543074246] **************************************************************** Lambda = 0.342 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.94293567e-02 -4.15238091e-01 -1.73440713e-01 6.62660592e-02 2.00041726e-01 1.14307547e-01 -1.70431941e-01 5.72700834e-02 -7.81205232e-03 3.85494957e-04] E, E_D, E_W= 8.72912023316 8.67775796397 0.0513622691909 Err = [0.342, 8.7291202331580848] **************************************************************** Lambda = 0.343 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.95592416e-02 -4.14671783e-01 -1.73318319e-01 6.60992255e-02 1.99914609e-01 1.14384768e-01 -1.70430733e-01 5.72634173e-02 -7.81077436e-03 3.85421137e-04] E, E_D, E_W= 8.72920615187 8.67778727747 0.0514188744009 Err = [0.343, 8.7292061518746493] **************************************************************** Lambda = 0.344 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.96660476e-02 -4.14109604e-01 -1.73197334e-01 6.59339350e-02 1.99787944e-01 1.14460799e-01 -1.70428929e-01 5.72566148e-02 -7.80948134e-03 3.85346666e-04] E, E_D, E_W= 8.72918940282 8.67771438875 0.0514750140657 Err = [0.344, 8.7291894028192676] **************************************************************** Lambda = 0.345 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.97869621e-02 -4.13551598e-01 -1.73074625e-01 6.57693041e-02 1.99661531e-01 1.14536295e-01 -1.70426901e-01 5.72497696e-02 -7.80818437e-03 3.85272052e-04] E, E_D, E_W= 8.72925752401 8.6777258537 0.0515316703052 Err = [0.345, 8.7292575240062398] **************************************************************** Lambda = 0.346 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -8.99200865e-02 -4.12985333e-01 -1.72954856e-01 6.56061993e-02 1.99535503e-01 1.14610742e-01 -1.70424349e-01 5.72428052e-02 -7.80687434e-03 3.85196877e-04] E, E_D, E_W= 8.72934433616 8.6777570518 0.0515872843647 Err = [0.346, 8.7293443361641341] **************************************************************** Lambda = 0.347 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.00358959e-02 -4.12425317e-01 -1.72834557e-01 6.54439804e-02 1.99409738e-01 1.14684602e-01 -1.70421546e-01 5.72357918e-02 -7.80555963e-03 3.85121526e-04] E, E_D, E_W= 8.72936598937 8.67772317328 0.0516428160842 Err = [0.347, 8.7293659893682083] **************************************************************** Lambda = 0.348 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.01504570e-02 -4.11871814e-01 -1.72712740e-01 6.52824188e-02 1.99284291e-01 1.14757768e-01 -1.70418424e-01 5.72287111e-02 -7.80423803e-03 3.85045896e-04] E, E_D, E_W= 8.7294003284 8.6777015922 0.051698736199 Err = [0.348, 8.729400328397011] **************************************************************** Lambda = 0.349 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.02774800e-02 -4.11313710e-01 -1.72592406e-01 6.51222066e-02 1.99159168e-01 1.14829983e-01 -1.70414829e-01 5.72215237e-02 -7.80290480e-03 3.84969769e-04] E, E_D, E_W= 8.72947110449 8.67771704278 0.0517540617129 Err = [0.349, 8.729471104492422] **************************************************************** Lambda = 0.35 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.03998633e-02 -4.10759848e-01 -1.72471337e-01 6.49630381e-02 1.99034467e-01 1.14901241e-01 -1.70410778e-01 5.72142357e-02 -7.80156080e-03 3.84893190e-04] E, E_D, E_W= 8.72953140134 8.67772200818 0.0518093931589 Err = [0.35, 8.7295314013423884] **************************************************************** Lambda = 0.351 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.05036965e-02 -4.10215423e-01 -1.72348267e-01 6.48040572e-02 1.98909798e-01 1.14972462e-01 -1.70406765e-01 5.72069684e-02 -7.80022018e-03 3.84816795e-04] E, E_D, E_W= 8.72952929439 8.6776643108 0.0518649835856 Err = [0.351, 8.7295292943905114] **************************************************************** Lambda = 0.352 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.06186151e-02 -4.09668864e-01 -1.72227322e-01 6.46466473e-02 1.98785445e-01 1.15042800e-01 -1.70402327e-01 5.71996074e-02 -7.79886957e-03 3.84739981e-04] E, E_D, E_W= 8.72957617907 8.67765583259 0.0519203464735 Err = [0.352, 8.7295761790678501] **************************************************************** Lambda = 0.353 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.07421270e-02 -4.09118686e-01 -1.72106952e-01 6.44901408e-02 1.98661376e-01 1.15112474e-01 -1.70397586e-01 5.71921829e-02 -7.79751245e-03 3.84662905e-04] E, E_D, E_W= 8.72964135379 8.67766625665 0.0519750971317 Err = [0.353, 8.729641353785258] **************************************************************** Lambda = 0.354 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.08611054e-02 -4.08573155e-01 -1.71985044e-01 6.43344421e-02 1.98537872e-01 1.15180942e-01 -1.70392249e-01 5.71846226e-02 -7.79614038e-03 3.84585184e-04] E, E_D, E_W= 8.72968904555 8.67765918708 0.0520298584699 Err = [0.354, 8.7296890455522718] **************************************************************** Lambda = 0.355 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.09782725e-02 -4.08032053e-01 -1.71864167e-01 6.41800577e-02 1.98414517e-01 1.15248892e-01 -1.70386681e-01 5.71770160e-02 -7.79476382e-03 3.84507291e-04] E, E_D, E_W= 8.72975666474 8.67767178528 0.0520848794552 Err = [0.355, 8.7297566647359375] **************************************************************** Lambda = 0.356 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.10972594e-02 -4.07489350e-01 -1.71743322e-01 6.40262851e-02 1.98291472e-01 1.15316282e-01 -1.70380879e-01 5.71693639e-02 -7.79338294e-03 3.84429237e-04] E, E_D, E_W= 8.72981224296 8.6776729044 0.0521393385567 Err = [0.356, 8.7298122429604543] **************************************************************** Lambda = 0.357 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.12125740e-02 -4.06949531e-01 -1.71622484e-01 6.38733674e-02 1.98168439e-01 1.15383425e-01 -1.70374988e-01 5.71616999e-02 -7.79200155e-03 3.84351189e-04] E, E_D, E_W= 8.72985535861 8.6776616439 0.052193714711 Err = [0.357, 8.7298553586134862] **************************************************************** Lambda = 0.358 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.13333771e-02 -4.06415605e-01 -1.71500198e-01 6.37215546e-02 1.98046125e-01 1.15449013e-01 -1.70368308e-01 5.71538521e-02 -7.79059955e-03 3.84272239e-04] E, E_D, E_W= 8.7299485288 8.67769991622 0.0522486125783 Err = [0.358, 8.7299485288006586] **************************************************************** Lambda = 0.359 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.14383881e-02 -4.05885376e-01 -1.71378076e-01 6.35705007e-02 1.97923989e-01 1.15514214e-01 -1.70361477e-01 5.71459784e-02 -7.78919546e-03 3.84193227e-04] E, E_D, E_W= 8.72997413106 8.67767096787 0.0523031631851 Err = [0.359, 8.72997413105562] **************************************************************** Lambda = 0.36 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.15454159e-02 -4.05353160e-01 -1.71257443e-01 6.34202498e-02 1.97801850e-01 1.15579392e-01 -1.70354703e-01 5.71381309e-02 -7.78779550e-03 3.84114437e-04] E, E_D, E_W= 8.72999216549 8.67763494574 0.0523572197552 Err = [0.36, 8.7299921654907333] **************************************************************** Lambda = 0.361 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.16591377e-02 -4.04820445e-01 -1.71136705e-01 6.32712515e-02 1.97680206e-01 1.15643279e-01 -1.70347262e-01 5.71301278e-02 -7.78637805e-03 3.84034880e-04] E, E_D, E_W= 8.73003576355 8.67762476156 0.0524110019985 Err = [0.361, 8.7300357635548789] **************************************************************** Lambda = 0.362 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.17910955e-02 -4.04283642e-01 -1.71017278e-01 6.31230295e-02 1.97558798e-01 1.15706804e-01 -1.70339702e-01 5.71221080e-02 -7.78495973e-03 3.83955320e-04] E, E_D, E_W= 8.73014999959 8.67768549522 0.0524645043751 Err = [0.362, 8.7301499995948184] **************************************************************** Lambda = 0.363 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.18672413e-02 -4.03772709e-01 -1.70892842e-01 6.29754298e-02 1.97437837e-01 1.15769199e-01 -1.70331565e-01 5.71139549e-02 -7.78352656e-03 3.83875115e-04] E, E_D, E_W= 8.73009185189 8.67757264119 0.0525192107 Err = [0.363, 8.7300918518925901] **************************************************************** Lambda = 0.364 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.19913564e-02 -4.03245573e-01 -1.70771836e-01 6.28289764e-02 1.97317079e-01 1.15831172e-01 -1.70323266e-01 5.71057738e-02 -7.78209111e-03 3.83794839e-04] E, E_D, E_W= 8.73019759374 8.67762460327 0.0525729904707 Err = [0.364, 8.7301975937388576] **************************************************************** Lambda = 0.365 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.21069512e-02 -4.02714781e-01 -1.70653336e-01 6.26837062e-02 1.97196608e-01 1.15892488e-01 -1.70314672e-01 5.70975317e-02 -7.78064950e-03 3.83714318e-04] E, E_D, E_W= 8.73023790273 8.67761217249 0.0526257302392 Err = [0.365, 8.7302379027292059] **************************************************************** Lambda = 0.366 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.22315105e-02 -4.02192104e-01 -1.70531373e-01 6.25386323e-02 1.97076246e-01 1.15953486e-01 -1.70305948e-01 5.70892662e-02 -7.77920593e-03 3.83633735e-04] E, E_D, E_W= 8.73034901121 8.67766964464 0.0526793665711 Err = [0.366, 8.7303490112111302] **************************************************************** Lambda = 0.367 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.23309366e-02 -4.01674491e-01 -1.70410737e-01 6.23946996e-02 1.96956209e-01 1.16013761e-01 -1.70296890e-01 5.70809295e-02 -7.77775494e-03 3.83552846e-04] E, E_D, E_W= 8.73035713217 8.67762452409 0.0527326080755 Err = [0.367, 8.7303571321687166] **************************************************************** Lambda = 0.368 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.24464273e-02 -4.01155173e-01 -1.70289415e-01 6.22515869e-02 1.96836694e-01 1.16072994e-01 -1.70287332e-01 5.70724811e-02 -7.77629186e-03 3.83471443e-04] E, E_D, E_W= 8.73042796949 8.67764226199 0.0527857075047 Err = [0.368, 8.7304279694932898] **************************************************************** Lambda = 0.369 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.25355888e-02 -4.00650743e-01 -1.70166390e-01 6.21092791e-02 1.96717247e-01 1.16131912e-01 -1.70277652e-01 5.70640128e-02 -7.77482730e-03 3.83390003e-04] E, E_D, E_W= 8.73043401371 8.67759437841 0.0528396352942 Err = [0.369, 8.730434013708523] **************************************************************** Lambda = 0.37 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.26585940e-02 -4.00128364e-01 -1.70046792e-01 6.19678647e-02 1.96598175e-01 1.16190172e-01 -1.70267682e-01 5.70554849e-02 -7.77335675e-03 3.83308324e-04] E, E_D, E_W= 8.7305146024 8.67762268931 0.052891913094 Err = [0.37, 8.7305146024036979] **************************************************************** Lambda = 0.371 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.27771788e-02 -3.99608360e-01 -1.69927238e-01 6.18273482e-02 1.96479287e-01 1.16247828e-01 -1.70257419e-01 5.70468926e-02 -7.77187937e-03 3.83226360e-04] E, E_D, E_W= 8.73057957853 8.6776355621 0.0529440164263 Err = [0.371, 8.7305795785259601] **************************************************************** Lambda = 0.372 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.28764802e-02 -3.99105047e-01 -1.69805533e-01 6.16878605e-02 1.96360875e-01 1.16304501e-01 -1.70246692e-01 5.70381981e-02 -7.77039102e-03 3.83143932e-04] E, E_D, E_W= 8.73062845361 8.67763103864 0.0529974149722 Err = [0.372, 8.7306284536134022] **************************************************************** Lambda = 0.373 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.29848230e-02 -3.98591645e-01 -1.69685961e-01 6.15487655e-02 1.96242386e-01 1.16361405e-01 -1.70236151e-01 5.70295607e-02 -7.76891030e-03 3.83061882e-04] E, E_D, E_W= 8.7306621111 8.67761268524 0.0530494258624 Err = [0.373, 8.730662111100596] **************************************************************** Lambda = 0.374 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.30859552e-02 -3.98085288e-01 -1.69565426e-01 6.14109271e-02 1.96124403e-01 1.16417010e-01 -1.70224943e-01 5.70207674e-02 -7.76741207e-03 3.82979065e-04] E, E_D, E_W= 8.7306902308 8.67758845655 0.0531017742506 Err = [0.374, 8.7306902308032992] **************************************************************** Lambda = 0.375 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.32059767e-02 -3.97575822e-01 -1.69445142e-01 6.12735538e-02 1.96006523e-01 1.16472494e-01 -1.70213738e-01 5.70119864e-02 -7.76591625e-03 3.82896390e-04] E, E_D, E_W= 8.73078893483 8.67763494774 0.053153987091 Err = [0.375, 8.7307889348284231] **************************************************************** Lambda = 0.376 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.33092331e-02 -3.97074304e-01 -1.69323794e-01 6.11370013e-02 1.95889029e-01 1.16527105e-01 -1.70202104e-01 5.70031089e-02 -7.76440992e-03 3.82813264e-04] E, E_D, E_W= 8.73083743075 8.67763107752 0.053206353235 Err = [0.376, 8.7308374307514729] **************************************************************** Lambda = 0.377 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.34068510e-02 -3.96578335e-01 -1.69201201e-01 6.10010835e-02 1.95771949e-01 1.16580895e-01 -1.70190082e-01 5.69941464e-02 -7.76289452e-03 3.82729758e-04] E, E_D, E_W= 8.73087303707 8.67761414135 0.0532588957197 Err = [0.377, 8.730873037072266] **************************************************************** Lambda = 0.378 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.35264343e-02 -3.96072750e-01 -1.69081738e-01 6.08662462e-02 1.95654891e-01 1.16634644e-01 -1.70178110e-01 5.69852088e-02 -7.76138307e-03 3.82646466e-04] E, E_D, E_W= 8.73097656433 8.67766591706 0.0533106472674 Err = [0.378, 8.730976564327964] **************************************************************** Lambda = 0.379 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.36193166e-02 -3.95577800e-01 -1.68960424e-01 6.07321983e-02 1.95538241e-01 1.16687355e-01 -1.70165609e-01 5.69761483e-02 -7.75985792e-03 3.82562577e-04] E, E_D, E_W= 8.73098345041 8.67762091042 0.0533625399906 Err = [0.379, 8.7309834504127615] **************************************************************** Lambda = 0.38 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.37281305e-02 -3.95079510e-01 -1.68840346e-01 6.05988495e-02 1.95421758e-01 1.16739837e-01 -1.70153052e-01 5.69670860e-02 -7.75833352e-03 3.82478754e-04] E, E_D, E_W= 8.73105389967 8.67763966224 0.053414237424 Err = [0.38, 8.7310538996674971] **************************************************************** Lambda = 0.381 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.38338079e-02 -3.94583078e-01 -1.68720149e-01 6.04664796e-02 1.95305737e-01 1.16791252e-01 -1.70139960e-01 5.69579007e-02 -7.75679550e-03 3.82394340e-04] E, E_D, E_W= 8.73110821103 8.67764249588 0.0534657151522 Err = [0.381, 8.7311082110277383] **************************************************************** Lambda = 0.382 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.39379320e-02 -3.94089401e-01 -1.68599681e-01 6.03344717e-02 1.95189751e-01 1.16842712e-01 -1.70126960e-01 5.69487494e-02 -7.75526238e-03 3.82310180e-04] E, E_D, E_W= 8.73116420624 8.67764700264 0.0535172035916 Err = [0.382, 8.731164206236242] **************************************************************** Lambda = 0.383 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.40527152e-02 -3.93591611e-01 -1.68480200e-01 6.02035406e-02 1.95074060e-01 1.16893426e-01 -1.70113603e-01 5.69395201e-02 -7.75372098e-03 3.82225672e-04] E, E_D, E_W= 8.73124870574 8.67768054699 0.0535681587511 Err = [0.383, 8.7312487057370465] **************************************************************** Lambda = 0.384 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.41442637e-02 -3.93102590e-01 -1.68360733e-01 6.00735944e-02 1.94958700e-01 1.16943377e-01 -1.70099883e-01 5.69302107e-02 -7.75217100e-03 3.82140804e-04] E, E_D, E_W= 8.73125504265 8.67763580335 0.0536192393021 Err = [0.384, 8.7312550426544675] **************************************************************** Lambda = 0.385 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.42475785e-02 -3.92616830e-01 -1.68238804e-01 5.99437719e-02 1.94843634e-01 1.16992860e-01 -1.70085969e-01 5.69208644e-02 -7.75061760e-03 3.82055811e-04] E, E_D, E_W= 8.73132146202 8.6776507405 0.0536707215109 Err = [0.385, 8.7313214620157922] **************************************************************** Lambda = 0.386 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.43484090e-02 -3.92126949e-01 -1.68120105e-01 5.98152202e-02 1.94728676e-01 1.17042041e-01 -1.70071950e-01 5.69115024e-02 -7.74906322e-03 3.81970802e-04] E, E_D, E_W= 8.73136169741 8.67764032604 0.0537213713674 Err = [0.386, 8.7313616974051644] **************************************************************** Lambda = 0.387 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.44590790e-02 -3.91638900e-01 -1.68000422e-01 5.96874091e-02 1.94614217e-01 1.17090140e-01 -1.70057387e-01 5.69020151e-02 -7.74749492e-03 3.81885188e-04] E, E_D, E_W= 8.73145011206 8.67767789541 0.0537722166418 Err = [0.387, 8.7314501120558408] **************************************************************** Lambda = 0.388 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.45636696e-02 -3.91155617e-01 -1.67879519e-01 5.95598649e-02 1.94499767e-01 1.17138303e-01 -1.70042923e-01 5.68925636e-02 -7.74593175e-03 3.81799838e-04] E, E_D, E_W= 8.73152130358 8.67769814522 0.0538231583573 Err = [0.388, 8.7315213035821468] **************************************************************** Lambda = 0.389 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.46697452e-02 -3.90669749e-01 -1.67760056e-01 5.94334901e-02 1.94385819e-01 1.17185340e-01 -1.70027894e-01 5.68829814e-02 -7.74435402e-03 3.81713854e-04] E, E_D, E_W= 8.73158435259 8.67771086369 0.0538734889071 Err = [0.389, 8.7315843525921615] **************************************************************** Lambda = 0.39 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.47520068e-02 -3.90197891e-01 -1.67638615e-01 5.93074926e-02 1.94271944e-01 1.17232320e-01 -1.70012898e-01 5.68734188e-02 -7.74277952e-03 3.81628046e-04] E, E_D, E_W= 8.73159113378 8.67766651755 0.0539246162253 Err = [0.39, 8.7315911337764103] **************************************************************** Lambda = 0.391 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.48528200e-02 -3.89718748e-01 -1.67518300e-01 5.91823382e-02 1.94158476e-01 1.17278433e-01 -1.69997481e-01 5.68637614e-02 -7.74119466e-03 3.81541796e-04] E, E_D, E_W= 8.73164832838 8.67767329302 0.0539750353592 Err = [0.391, 8.7316483283824624] **************************************************************** Lambda = 0.392 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.49609468e-02 -3.89238500e-01 -1.67398206e-01 5.90577835e-02 1.94045053e-01 1.17324415e-01 -1.69982046e-01 5.68541095e-02 -7.73961131e-03 3.81455642e-04] E, E_D, E_W= 8.73172908303 8.67770383784 0.0540252451894 Err = [0.392, 8.7317290830323877] **************************************************************** Lambda = 0.393 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.50689883e-02 -3.88756037e-01 -1.67280569e-01 5.89345314e-02 1.93932150e-01 1.17369351e-01 -1.69966103e-01 5.68443431e-02 -7.73801540e-03 3.81368949e-04] E, E_D, E_W= 8.73180187789 8.67772695284 0.0540749250449 Err = [0.393, 8.7318018778895024] **************************************************************** Lambda = 0.394 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.51602443e-02 -3.88287057e-01 -1.67159187e-01 5.88114508e-02 1.93819349e-01 1.17414015e-01 -1.69950058e-01 5.68345601e-02 -7.73641832e-03 3.81282228e-04] E, E_D, E_W= 8.731841455 8.67771600657 0.0541254484249 Err = [0.394, 8.7318414549985803] **************************************************************** Lambda = 0.395 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.52684747e-02 -3.87807172e-01 -1.67041016e-01 5.86891936e-02 1.93706686e-01 1.17458514e-01 -1.69933990e-01 5.68247831e-02 -7.73482287e-03 3.81195611e-04] E, E_D, E_W= 8.7319143493 8.67773951076 0.0541748385455 Err = [0.395, 8.7319143493049207] **************************************************************** Lambda = 0.396 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.53684920e-02 -3.87333570e-01 -1.66921907e-01 5.85679966e-02 1.93594510e-01 1.17501804e-01 -1.69917300e-01 5.68148598e-02 -7.73321091e-03 3.81108269e-04] E, E_D, E_W= 8.731968841 8.67774439281 0.0542244481816 Err = [0.396, 8.7319688409961405] **************************************************************** Lambda = 0.397 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.54585156e-02 -3.86870549e-01 -1.66800667e-01 5.84468641e-02 1.93482413e-01 1.17545158e-01 -1.69900713e-01 5.68049736e-02 -7.73160425e-03 3.81021198e-04] E, E_D, E_W= 8.73202059027 8.67774574033 0.0542748499435 Err = [0.397, 8.7320205902727466] **************************************************************** Lambda = 0.398 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.55672120e-02 -3.86395094e-01 -1.66682273e-01 5.83266840e-02 1.93370660e-01 1.17587769e-01 -1.69883773e-01 5.67950092e-02 -7.72998919e-03 3.80933771e-04] E, E_D, E_W= 8.73209422056 8.67777032385 0.0543238967129 Err = [0.398, 8.7320942205589986] **************************************************************** Lambda = 0.399 feature_names= ['x^0', 'x^1', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8', 'x^9'] coef= [ -9.56625994e-02 -3.85932971e-01 -1.66561508e-01 5.82069795e-02 1.93258913e-01 1.17630375e-01 -1.69866892e-01 5.67850706e-02 -7.72837806e-03 3.80846554e-04] E, E_D, E_W= 8.73216630897 8.67779229061 0.05437401836 Err = [0.399, 8.7321663089728485] **************************************************************** min_e = [0.251, 8.7268423778143465]
In [23]:
plt.plot(L, np.log(E_T) , 'b-')
plt.title('$Lambda \ vs. Error, blue:E(w)=E_D+E_W$')
plt.xlabel('lambda')
plt.ylabel('log( E(w) )')
Out[23]:
<matplotlib.text.Text at 0xf0aa748>
In [59]:
Err = pd.DataFrame({'lambda':L,
'log(E)':np.log(E_T)})
Err
Out[59]:
| lambda | log(E) | |
|---|---|---|
| 0 | 0.200 | 2.166624 |
| 1 | 0.201 | 2.166619 |
| 2 | 0.202 | 2.166607 |
| 3 | 0.203 | 2.166600 |
| 4 | 0.204 | 2.166598 |
| 5 | 0.205 | 2.166589 |
| 6 | 0.206 | 2.166585 |
| 7 | 0.207 | 2.166577 |
| 8 | 0.208 | 2.166558 |
| 9 | 0.209 | 2.166553 |
| 10 | 0.210 | 2.166554 |
| 11 | 0.211 | 2.166546 |
| 12 | 0.212 | 2.166542 |
| 13 | 0.213 | 2.166527 |
| 14 | 0.214 | 2.166526 |
| 15 | 0.215 | 2.166516 |
| 16 | 0.216 | 2.166507 |
| 17 | 0.217 | 2.166516 |
| 18 | 0.218 | 2.166496 |
| 19 | 0.219 | 2.166498 |
| 20 | 0.220 | 2.166500 |
| 21 | 0.221 | 2.166487 |
| 22 | 0.222 | 2.166480 |
| 23 | 0.223 | 2.166483 |
| 24 | 0.224 | 2.166474 |
| 25 | 0.225 | 2.166463 |
| 26 | 0.226 | 2.166470 |
| 27 | 0.227 | 2.166471 |
| 28 | 0.228 | 2.166459 |
| 29 | 0.229 | 2.166458 |
| ... | ... | ... |
| 170 | 0.370 | 2.166824 |
| 171 | 0.371 | 2.166832 |
| 172 | 0.372 | 2.166837 |
| 173 | 0.373 | 2.166841 |
| 174 | 0.374 | 2.166844 |
| 175 | 0.375 | 2.166856 |
| 176 | 0.376 | 2.166861 |
| 177 | 0.377 | 2.166865 |
| 178 | 0.378 | 2.166877 |
| 179 | 0.379 | 2.166878 |
| 180 | 0.380 | 2.166886 |
| 181 | 0.381 | 2.166892 |
| 182 | 0.382 | 2.166899 |
| 183 | 0.383 | 2.166908 |
| 184 | 0.384 | 2.166909 |
| 185 | 0.385 | 2.166917 |
| 186 | 0.386 | 2.166921 |
| 187 | 0.387 | 2.166931 |
| 188 | 0.388 | 2.166940 |
| 189 | 0.389 | 2.166947 |
| 190 | 0.390 | 2.166948 |
| 191 | 0.391 | 2.166954 |
| 192 | 0.392 | 2.166963 |
| 193 | 0.393 | 2.166972 |
| 194 | 0.394 | 2.166976 |
| 195 | 0.395 | 2.166985 |
| 196 | 0.396 | 2.166991 |
| 197 | 0.397 | 2.166997 |
| 198 | 0.398 | 2.167005 |
| 199 | 0.399 | 2.167013 |
200 rows × 2 columns
In [ ]:
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