Revised_Simplex_Py/LPdata.py at master · ashu8826/Revised_Simplex_Py · GitHub

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# In[23]:
#==============================================================================
# #LP Maximization Instances
# # unbounded example
# A = [[2,-2,1,0],
#      [4,0,0,1]]
# c = [4,6,0,0]
# b = [6,16]
# # In[23]:
# # general example
# A = [[1,1,1,0,0],
#      [2,3,0,1,0],
#      [1,5,0,0,1]]
# c = [6,8,0,0,0]
# b = [10,25,35]
#
# # In[23]:
# #needs artifical variable
# A = [[1,2,3,0],
#      [-1,2,6,0],
#      [0,4,9,0],
#      [0,0,3,1]]
# b = [3,2,5,1]
# c = [1,1,1,0]
#==============================================================================
# In[23]:
import numpy as np
import random

train_data = np.load("dataset\DB_Vecs_100.npy")
train_label = np.load("dataset\DB_Labels_100.npy")
test_data = np.load("dataset\Q_vecs_100.npy")
train_label = train_label.reshape((train_label.shape[0],1))

data_label = np.concatenate((train_data,train_label),axis = 1)
uni= np.unique(data_label, axis=0)
train_data = uni[uni[:,-1].argsort()]

train_data = np.delete(train_data, -1, 1)
train_label = np.array(uni[:,-1]).reshape((train_data.shape[0],1))

train_label[train_label==0] = -1

no_train_sample = train_data.shape[0]
no_variable = train_data.shape[1] + 1

c = [0]*(no_variable*2) + [-1]*no_train_sample + [0]*no_train_sample
b = [-1]*no_train_sample


U = np.diag([-1]*no_train_sample)
S = np.diag([1]*no_train_sample)
A = np.zeros((no_train_sample,no_variable*2))
one = np.array([random.gauss(1, 0.1) for i in range(no_train_sample)]).reshape((no_train_sample,1))
Z = train_label*np.concatenate((train_data,one),axis = 1)

for i in range(no_variable):
    A[:,2*i] = Z[:,i]
    A[:,2*i+1] = -1*Z[:,i]

A = np.concatenate((A,U),axis = 1)
A = np.concatenate((A,S),axis = 1)

# In[24]:
#==============================================================================
# import numpy as np
#
# train_data = np.load("dataset\DB_Vecs.npy")
# train_label = np.load("dataset\DB_Labels.npy")
# test_data = np.load("dataset\Q_vecs.npy")
# train_label = train_label.reshape((train_label.shape[0],1))
#
# data_label = np.concatenate((train_data,train_label),axis = 1)
# uni,unique_indices,unique_inverse,unique_counts = np.unique(data_label,return_index=True, return_inverse=True, return_counts=True, axis=0)
# uni = uni[uni[:,-1].argsort()]
#
# train_data = uni
# train_data = np.delete(train_data, -1, 1)
# train_label = np.array(uni[:,-1]).reshape((train_data.shape[0],1))
#
# train_label[train_label==0] = -1
#no_train_sample = train_data.shape[0]
#no_variable = train_data.shape[1] + 1
# b = [-1]*no_train_sample
#
# U = np.diag([-1]*no_train_sample)
# S = np.diag([1]*no_train_sample)
#
# Z = train_label*train_data
# A = np.zeros((no_train_sample,60))
#
# for i in range(30):
#     A[:,2*i] = Z[:,i]
#     A[:,2*i+1] = -1*Z[:,i]
# c = [0]*60 + [-1]*no_train_sample + [0]*no_train_sample
# A = np.concatenate((A,U),axis = 1)
# A = np.concatenate((A,S),axis = 1)
#
#
#==============================================================================