dsalaj

import numpy as np

x = [1, 2, 3.5, 4]
y = [1, 2, 3, 3, 4, 5, 3]
z = [7, 8]
arrs = [x, y, z]

def tolerant_mean(arrs):
    # arrs = [x, y, z]
    lens = [len(i) for i in arrs]

dsalaj

Keybase proof

I hereby claim:

• I am dsalaj on github.
• I am dsalaj (https://keybase.io/dsalaj) on keybase.
• I have a public key ASBBDtsHlfUOlqCUF48dL0qkNY-lWwrLC2dbOWrHjNMYrwo

To claim this, I am signing this object:

dsalaj

import tensorflow as tf

x_train = [i for i in range(0, 20, 2)]  # even
x_val = [i for i in range(1, 20, 2)]  # odd
y_train = [i**2 for i in x_train]  # squared
y_val = [i**2 for i in x_val]

def gen_data_epoch(test=False):  # parametrized generator
    train_data = x_val if test else x_train
    label_data = y_val if test else y_train

dsalaj

#!/bin/bash
#SBATCH --job-name=GSC                  # Job name
#SBATCH --mail-type=END,FAIL            # Mail events (NONE, BEGIN, END, FAIL, ALL)
#SBATCH --mail-user=salaj.au@gmail.com  # Where to send mail
#SBATCH --output=slurm_out_%j.log       # Standard output and error log
#SBATCH --nodes=1
#SBATCH --exclusive
#SBATCH --partition=IGIcrunchers

conda activate venv2

dsalaj

def split_dataset(ds, version=1):
    if version == 1:
        train_ds = ds.dataset.shard(num_shards=4, index=0)
        train_ds.concatenate(ds.dataset.shard(num_shards=4, index=1))
        train_ds.concatenate(ds.dataset.shard(num_shards=4, index=2))
        valid_ds = ds.dataset.shard(num_shards=4, index=3)
        return train_ds, valid_ds

    elif version == 2:
        def is_val(x, y):

dsalaj

# filter with strings
df.filter(df.name.endswith('ice')).collect()
# [Row(age=2, name='Alice')]

# order with null values at the end
df.select(df.name).orderBy(df.name.desc_nulls_last()).collect()
# [Row(name='Tom'), Row(name='Alice'), Row(name=None)]

# filter by null
df.filter(df.height.isNotNull()).collect()