CoCalc -- test_sqlite3_classification_database

Path: Boolean-Cayley-graphs/sage-code / test_sqlite3_classification_database_prototypes.ipynb

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Kernel: SageMath 9.5

In [1]:

import datetime
import os
import sqlite3

from boolean_cayley_graphs.bent_function import BentFunction
from boolean_cayley_graphs.classification_database_sqlite3 import *
from boolean_cayley_graphs.bent_function_cayley_graph_classification import BentFunctionCayleyGraphClassification

In [2]:

db_dirname=os.path.join("..","db")
sobj_dirname=os.path.join("..","sobj")

In [3]:

rm -f test_p6.db

In [4]:

conn = create_database("test_p6.db")
conn = create_classification_tables("test_p6.db")

In [5]:

for i in range(1,5):
    print(i)
    name = "p6_"+str(i)
    cgc = BentFunctionCayleyGraphClassification.load_mangled(name + ".sobj", dir=sobj_dirname)
    insert_classification(conn, cgc, name)

In [6]:

cgc.report()

Algebraic normal form of Boolean function: x0*x1*x2 + x0*x3 + x1*x3*x4 + x1*x5 + x2*x3*x5 + x2*x3 + x2*x4 + x2*x5 + x3*x4 + x3*x5
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 64, 28, 12))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 3 extended Cayley classes in the extended translation class.

In [7]:

bentf = BentFunction(cgc.algebraic_normal_form)
c = select_classification_where_bent_function(conn, bentf)
c.report()

Algebraic normal form of Boolean function: x0*x1*x2 + x0*x3 + x1*x3*x4 + x1*x5 + x2*x3*x5 + x2*x3 + x2*x4 + x2*x5 + x3*x4 + x3*x5
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 64, 28, 12))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 3 extended Cayley classes in the extended translation class.

In [8]:

c = select_classification_where_name(conn, "p6_1")
c.report()

Algebraic normal form of Boolean function: x0*x1 + x2*x3 + x4*x5
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 64, 28, 12))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 2 extended Cayley classes in the extended translation class.

In [9]:

curs = conn.cursor()
print(datetime.datetime.now(), "before")
curs.execute("SELECT COUNT(ROWID) FROM cayley_graph")
print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x)

2022-05-31 23:05:39.480172 before
2022-05-31 23:05:39.481183 after
12

In [10]:

print(datetime.datetime.now(), "before")
curs.execute("SELECT COUNT(ROWID) FROM graph")
print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x)

2022-05-31 23:05:39.552459 before
2022-05-31 23:05:39.554187 after
11

In [11]:

conn.close()

In [12]:

rm -f test_p8.db

In [13]:

conn = create_database("test_p8.db")
conn = create_classification_tables("test_p8.db")

In [14]:

for i in range(1,11):
    name = "p8_"+str(i)
    cgc = BentFunctionCayleyGraphClassification.load_mangled(name + ".sobj", dir=sobj_dirname)
    print(datetime.datetime.now(), i)
    insert_classification(conn, cgc, name)
print(datetime.datetime.now())

2022-05-31 23:05:40.493969 1
2022-05-31 23:05:41.259589 2
2022-05-31 23:05:42.262146 3
2022-05-31 23:05:43.169159 4
2022-05-31 23:05:44.078996 5
2022-05-31 23:05:44.977874 6
2022-05-31 23:05:46.136206 7
2022-05-31 23:05:47.140389 8
2022-05-31 23:05:49.012154 9
2022-05-31 23:05:50.591327 10
2022-05-31 23:05:51.595664

In [15]:

c8_5 = select_classification_where_name(conn, "p8_5")
c8_5.report()

Algebraic normal form of Boolean function: x0*x1*x2 + x0*x6 + x1*x3*x4 + x1*x4 + x1*x5 + x2*x3*x5 + x2*x4 + x3*x7
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 256, 120, 56))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 9 extended Cayley classes in the extended translation class.

In [16]:

bentf = BentFunction(c8_5.algebraic_normal_form)

c = select_classification_where_bent_function(conn, bentf)
c.report()

Algebraic normal form of Boolean function: x0*x1*x2 + x0*x6 + x1*x3*x4 + x1*x4 + x1*x5 + x2*x3*x5 + x2*x4 + x3*x7
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 256, 120, 56))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 9 extended Cayley classes in the extended translation class.

In [17]:

c8_6 = select_classification_where_name(conn, "p8_6")
c8_6.report()

Algebraic normal form of Boolean function: x0*x1*x2 + x0*x2 + x0*x3 + x1*x3*x4 + x1*x6 + x2*x3*x5 + x2*x4 + x5*x7
Function is bent.

SDP design incidence structure t-design parameters: (True, (2, 256, 120, 56))

Classification of Cayley graphs and classification of Cayley graphs of duals are the same:

There are 9 extended Cayley classes in the extended translation class.

In [18]:

curs = conn.cursor()
print(datetime.datetime.now(), "before")
curs.execute("SELECT COUNT(*) FROM cayley_graph")
print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x)

2022-05-31 23:05:55.554799 before
2022-05-31 23:05:55.555912 after
66

In [19]:

curs = conn.cursor()
print(datetime.datetime.now(), "before")
curs.execute("SELECT COUNT(ROWID) FROM cayley_graph")
print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x)

2022-05-31 23:05:55.619528 before
2022-05-31 23:05:55.621320 after
66

In [20]:

print(datetime.datetime.now(), "before")
curs.execute("SELECT COUNT(ROWID) FROM graph")
print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x)

2022-05-31 23:05:55.682701 before
2022-05-31 23:05:55.684404 after
55

In [21]:

conn.close()

In [22]:

conn = connect_to_database('test_p8.db')

In [23]:

curs = conn.cursor()
print(datetime.datetime.now(), "before")
curs.execute("""
select name, cayley_graph_index, graph_id, count(*)
from matrices, (
    select name, bent_function, cayley_graph_index, graph_id
    from (
        select canonical_label_hash
        from cayley_graph
        group by canonical_label_hash
        having count (canonical_label_hash) > 1) as repeats
    natural join cayley_graph
    natural join graph
    natural join bent_function )
as repeats_with_counts
where matrices.bent_function = repeats_with_counts.bent_function
and matrices.bent_cayley_graph_index = repeats_with_counts.cayley_graph_index
group by name, cayley_graph_index, graph_id
order by graph_id
""")
print(datetime.datetime.now(), "after")
for row in curs:
    for x in row:
        print(x,end=" ")
    print("")

2022-05-31 23:05:55.869847 before
2022-05-31 23:05:57.295262 after
p8_1 0 1 34816 
p8_2 0 1 6144 
p8_1 1 2 30720 
p8_2 3 2 2048 
p8_5 0 17 4096 
p8_6 0 17 4096 
p8_5 1 18 6144 
p8_6 1 18 6144 
p8_5 2 19 6144 
p8_6 2 19 6144 
p8_5 3 20 2048 
p8_6 5 20 2048 
p8_5 4 21 2048 
p8_6 8 21 2048 
p8_5 5 22 6144 
p8_6 6 22 6144 
p8_5 6 23 6144 
p8_6 7 23 6144 
p8_5 7 24 16384 
p8_6 3 24 16384 
p8_5 8 25 16384 
p8_6 4 25 16384 

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