def get_feature_deletion_results(Input_dimension ,Output_dimension ,array1,array2,mapping_scheme=1,max_value=0):
batch_error = []
sample_size = Input_dimension/100
reduced_input_dim = Input_dimension/1.5
demo_operator = operator(input_dim=Input_dimension, output_dim=Output_dimension, mapping_scheme=mapping_scheme)
demo_operator.mapping.bits = default_bits
demo_operator.mapping.map = default_maps
batch_inner_product1 = []
batch_inner_product2 = []
while Input_dimension >= reduced_input_dim:
# print ("epoch1:::Input Dimenson::",Input_dimension)
batch_feature_size = int(sample_size)
batch_positions = get_adversarial_positions(demo_operator,batch_feature_size)
Input_dimension-=batch_feature_size
array1,array2 = demo_operator.batch_delete_feature(batch_positions,array1,array2)
# print("batch feature deletion done....")
# print("arr1:",array1)
# print("arr2:",array2)
inner_product1, inner_product2 = demo_operator.inner_product(array1, array2)
error = abs(inner_product1-inner_product2)
print ("inners products:",inner_product1,inner_product2)
print("error:", error)
batch_error.append(error)
batch_inner_product1.append(inner_product1)
batch_inner_product2.append(inner_product2)
# print ("Mapping scheme :",mapping_scheme,"::")
# print (demo_operator.get_feature_count())
return batch_error,batch_inner_product1,batch_inner_product2,array1,array2
def main():
input_size, output_size = 50000, 2000
demo_operator = operator(input_dim=input_size, output_dim=output_size)
arr1 = np.random.randint(0, high=50, size=input_size)
arr2 = np.random.randint(0, high=50, size=input_size)
#print("Array1:", arr1)
#print("Array2:", arr2)
demo_operator.inner_product(arr1, arr2)
arr1 = np.insert(arr1, 1, 2)
arr2 = np.insert(arr2, 1, 3)
demo_operator.insert_feature(1, arr1, arr2)
demo_operator.inner_product(arr1, arr2)
arr1 = np.delete(arr1, 0)
arr2 = np.delete(arr2, 0)
demo_operator.delete_feature(0, arr1, arr2)
demo_operator.inner_product(arr1, arr2)
def batch_feature_deletion_error(Input_dim=500,Output_dim=30,rate=10,array1=[],array2=[],mapping_scheme=1,max_val=1):
batch_error = []
sample_size = Input_dim/100
reduced_input_dim = Input_dim/4
demo_operator = operator(input_dim=Input_dim, output_dim=Output_dim, mapping_scheme=mapping_scheme)
batch_inner_product1 = []
batch_inner_product2 = []
while Input_dim >= reduced_input_dim:
print ("epoch1:::Input Dimenson::",Input_dim)
batch_feature_size = int(sample_size)
batch_positions = []
batch_value1 = []
batch_value2 = []
for i in range(batch_feature_size):
batch_positions.append(Input_dim-1)
batch_value1.append(random.randint(0,max_val))
batch_value2.append(random.randint(0,max_val))
Input_dim -= 1
array1,array2 = demo_operator.batch_delete_feature(batch_positions,array1,array2)
# print("batch feature deletion done....")
# print("arr1:",array1)
# print("arr2:",array2)
inner_product1, inner_product2 = demo_operator.inner_product(array1, array2)
error = abs(inner_product1-inner_product2)
print ("inners products:",inner_product1,inner_product2)
print("error:", error)
batch_error.append(error)
batch_inner_product1.append(inner_product1)
batch_inner_product2.append(inner_product2)
return batch_error,batch_inner_product1,batch_inner_product2,array1,array2
def get_remap_results(Input_dimension, Output_dimension, array1, array2,
mapping_scheme):
batch_error = []
batch_time = []
sample_size = Input_dimension / 50
reduced_input_dim = int(Input_dimension * 2)
demo_operator = operator(input_dim=Input_dimension,
output_dim=Output_dimension,
mapping_scheme=mapping_scheme)
# demo_operator.mapping.bits = default_bits
# demo_operator.mapping.map = default_maps
batch_inner_product1 = []
batch_inner_product2 = []
while Input_dimension <= reduced_input_dim:
# print ("epoch1:::Input Dimenson::",Input_dimension)
batch_feature_size = int(sample_size)
batch_positions = get_adversarial_positions(demo_operator,
batch_feature_size)
Input_dimension += batch_feature_size
feature1 = np.random.normal(0, 1, size=batch_feature_size)
feature2 = np.random.normal(0, 1, size=batch_feature_size)
t1 = time.time()
array1, array2 = demo_operator.batch_insert_feature(
batch_positions, array1, array2, feature1, feature2)
# print("batch feature deletion done....")
# print("arr1:",array1)
# print("arr2:",array2)
fresh_operator = operator(input_dim=Input_dimension,
output_dim=Output_dimension,
mapping_scheme=mapping_scheme)
inner_product1, inner_product2 = fresh_operator.inner_product(
array1, array2)
t2 = time.time()
error = abs(inner_product1 - inner_product2)
# print ("inners products:",inner_product1,inner_product2)
# print("error:", error)
batch_error.append(error)
batch_time.append(t2 - t1)
batch_inner_product1.append(inner_product1)
batch_inner_product2.append(inner_product2)
# print ("Mapping scheme :",mapping_scheme,"::")
# print (demo_operator.get_feature_count())
return batch_error, batch_time, batch_inner_product1, batch_inner_product2, array1, array2
def get_inner_product_results(array1, array2, input_dimension, output_dimension): i = 10 avg_inner_product1, avg_inner_product2 = 0, 0 while i > 0: demo_operator = operator(input_dim=input_dimension, output_dim=output_dimension, mapping_scheme = 3) inner_product1, inner_product2 = demo_operator.inner_product(array1, array2) avg_inner_product1+=inner_product1 avg_inner_product2+=inner_product2 i-=1 avg_inner_product1/=10 avg_inner_product2/=10 return avg_inner_product1, avg_inner_product2
def sample_error(N, M):
sample_size = 100
arr1 = np.random.randint(0, high=N * 2, size=N)
arr2 = np.random.randint(0, high=N * 2, size=N)
average_sample_error = 0
for i in range(sample_size):
demo_operator = operator(N, M)
inner_product1, inner_product2 = demo_operator.inner_product(
arr1, arr2)
average_sample_error += ((inner_product1 - inner_product2)**2)
average_sample_error /= sample_size
average_sample_error = average_sample_error**0.5
return average_sample_error
