tejainece

import numpy as np
from lightning.classification import SAGAClassifier
from scipy import sparse

from sklearn.datasets import load_iris, make_classification
from sklearn.linear_model.logistic import (
    LogisticRegression,
)

Rhomboid

#!/usr/bin/env python
#
# Mersenne Twister predictor
#
# Feed this program the output of any 32-bit MT19937 Mersenne Twister and
# after seeing 624 values it will correctly predict the rest.
#
# The values may come from any point in the sequence -- the program does not
# need to see the first 624 values, just *any* 624 consecutive values.  The
# seed used is also irrelevant, and it will work even if the generator was

tejainece

package main

//#include<stdio.h>
//void inC() {
//    printf("I am in C code now!\n");
//}
import "C"
import "fmt"

func main() {

barrucadu

; The stack pointer is going to be an index into the stack, and the
; stack is an array of words. The alternative would be to have the
; stack pointer me a pointer to memory, but this is perhaps a bit
; nicer, as where the stack actually lives is totally irrelevant.

@stack = global [1000 x i64] undef
@sp    = global i64 undef;

; Now we have the basic stack operations: push, pop, and peek. As can
; be seen from the definitions, LLVM is typed, which is really nice as

carlosefonseca

#!/usr/bin/env bash

# obtains all data tables from database
TS=`sqlite3 $1 "SELECT tbl_name FROM sqlite_master WHERE type='table' and tbl_name not like 'sqlite_%';"`

# exports each table to csv
for T in $TS; do

sqlite3 $1 <<!
.headers on