def M_fed_style(purpose,design,kernel,p,h,flag,fed_w):
'''
flag shows if design odd(1) or it isnt(0)
fed_w is array with weights of norming design (sum eq 1)
'''
n=len(design)
u=[[(point-purpose)**(2*j) for j in range(p*2)]for point in design]
w1=[K((point-purpose)/h,mode=kernel)/h for point in design]
for i in range(len(w1)):
w1[i]*=fed_w[i]
def c_s(k):
sum=0
for b in range(n):
sum+=u[b][k]*w1[b]
sum*=2
return sum
S=[c_s(k)for k in range(p*2)]
M=np.empty((p+1,p+1),dtype=np.float64)
from math import trunc
for i in range(p+1):
for j in range(p+1):
if trunc((i+j)%2)==0:
M[i,j]=S[int((i+j)/2)]
else:
M[i,j]=0
'''contrib. of nonsymmetric point'''
if flag==1:
M[0,0]+=K(0.,mode=kernel)/h
return M
def M_symm(purpose, design, kernel, p, h, odd):
n = len(design)
Fish = np.zeros((p + 1, p + 1))
Kui = [K((design[i] - purpose) / h, mode=kernel) / h for i in range(n)]
if odd == 0: #число точек - четное
for i in range(p + 1):
for j in range(p + 1):
s = i + j
if s % 2 == 1:
Fish[i, j] += 0
else:
Fish[i, j] += 2 * sum([(design[a] - purpose)**s * Kui[a]
for a in range(n)])
else: #число точек - нечетное, и последняя в плане - не имеет парную
for i in range(p + 1):
for j in range(p + 1):
s = i + j
if s % 2 == 0:
Fish[i, j] += 2 * sum([(design[a] - purpose)**s * Kui[a]
for a in range(n - odd)])
Fish[i, j] += Kui[n - 1] * (design[n - 1] - purpose)**s
return Fish
def M(purpose, design, kernel, p, h):
n = len(design)
M = np.zeros((p + 1, p + 1))
Kui = [K((design[i] - purpose) / h, mode=kernel) / h for i in range(n)]
for i in range(n):
f_x = get_f(purpose, design[i], p)
M += Kui[i] * np.dot(f_x, f_x.T)
return M
res1=[len(design)*check_one(purpose,design,dot,p,h,kernel)[1]for dot in design]
return res
#cond2=check_all(purpose=0,design=[-1, 0, 1],p=2,h=1,kernel='gauss')
#print(cond2)
'''
def d_x(purpose, design, dot, p, h, kernel):
m = M(purpose, design, kernel, p, h)
D = np.linalg.inv(m)
f_x = np.matrix([(dot - purpose)**(j) for j in range(p + 1)]).T
d_x = np.dot(f_x.T, D)
d_x = np.dot(d_x, f_x)
d_x *= len(design)
return float(d_x)
h = 1
kernl = 'unif'
x_axe = np.linspace(-1, 1, 500)
y_axe=[K(x_axe[i]/h,mode=kernl)/h*d_x(purpose=0,design=[-0.999,0.8356540663993193, 0.5641572515850596,-0.564263395062234, 0.7821410588028485,0.2955600789475211,-0.29399672797215587,0.0005596857669483513,0.9376458913026472,-0.93614466888028,-0.7889649466036868,0.9999999983452569],\
dot=x_axe[i],p=10,h=h,kernel=kernl)for i in range(len(x_axe))]
y_axe = list(map(lambda y: float(y), y_axe))
import sys
from kernel import K
data_info = []
data_cluster = 0
#data_matrix = []
for datum in sys.stdin:
datum = datum.strip().split()
if int(datum[2]) == 0:
data_info = [float(num) for num in datum[3:-1]]
data_cluster = datum[-1]
#data_matrix = [int(num) for num in datum[0:2]]
elif int(datum[2]) == 1:
info = [float(num) for num in datum[3:-1]]
cluster = datum[-1]
kernel = K(data_info, info)
#matrix = [int(num) for num in datum[0:2]]
print ''.join(str(f) + ' ' for f in data_info) + cluster, kernel
"""
cluster = 1
cluster_count = 0
sum_number = 0
feature = []
for datum in sys.stdin:
datum = datum.strip().split()
xi_cluster = datum[-2]
xi_K = datum[-1]
xi_feature = datum[0:-2]
if int(xi_cluster) != cluster:
try:
def minimax(dot):
dot = float(dot)
d = dx(dot)
w = K((dot - purpose) / h, mode=kernel) / h
return float(-w * d)
#!/usr/bin/python import sys # get kernel from kernel import K # read data for datum in sys.stdin: datum = datum.strip().split() #split data cluster and features features = datum[0:-1] cluster = datum[-1] #K(xi, xi) kernel = K(features, features) #MAP <features, (cluster (0), A, K(xi,xi))> print ''.join(f+' ' for f in features)+ str(0) , str(kernel)
def minimax(dot):
dot = float(dot)
d = dx(purpose, design, dot, p, h, kernel)
w = K((dot - purpose) / h, mode=kernel) / h
return float(-w * d)