chrLen = np.full(chrNum, 0)
for i in range(chrNum):
chrLen[i] = len(refList[i])
print("Read bam file:", bam)
ReadCount = np.full((chrNum, np.max(chrLen)), 0)
ReadCount = get_RC(bam, chrList, ReadCount)
for i in range(chrNum):
binNum = int(chrLen[i] / binSize) + 1
pos, RD, GC = ReadDepth(ReadCount[0], binNum, refList[i])
# plot(pos, RD)
#==========================================================step2. GC normalization
#正规化之后，将gc含量乘以10，以向RD靠拢
GC = np.array(GC, dtype='float32').reshape(1, -1)
normal_GC = Normalizer(norm='max').fit_transform(GC)
normal_GC = normal_GC.flatten()
'''
print(ReadCount)
print(len(ReadCount[0]))
myout5=open("rc.txt","w")
for i in range(len(ReadCount[0])):
myout5.write(str(ReadCount[0][i]))
'''
#========================================================== step3. input GroundTruthCNV
myin = open(
'/media/hth/Huang Tihao/data/lcdx/1000genomes/chr21/groundtruth/NA19238.txt',
'r')
line = myin.readline()
list1 = []
while line:
alpha * np.dot(A, Theta_old_vector))
return Theta_vector
user_num = 20
item_num = 100
dimension = 5
alpha = 0.1 # regularizer
beta = 0.1 #regularizer
mu = 0.001 #step size
lambda_ = 0.1 #step size
user_feature = np.random.normal(size=(user_num, dimension))
user_feature = Normalizer().fit_transform(user_feature)
user_feature_vector = user_feature.flatten()
adj = rbf_kernel(user_feature)
lap = csgraph.laplacian(adj, normed=False)
item_feature = np.random.normal(size=(item_num, dimension))
item_feature = Normalizer().fit_transform(item_feature)
Y = np.dot(user_feature, item_feature.T) + np.random.normal(
size=(user_num, item_num), scale=0.1)
A_true = np.kron(lap, np.identity(dimension))
A = np.identity(user_num * dimension)
Theta_matrix = np.zeros((user_num, dimension))
Theta_vector = Theta_matrix.flatten()
L = np.identity(user_num)
