jogam5

import subprocess
import time

def automate_cli_input(cli_command, input_values):
    # Start the CLI subprocess
    cli_process = subprocess.Popen(cli_command.split(), stdin=subprocess.PIPE, 
                                   stdout=subprocess.PIPE, universal_newlines=True)

    # Iterate over the expected input prompts
    for prompt, value in input_values.items():

jogam5

import shutil
import os
import json
from PIL import Image

def copy_from_to(file_path_from, file_path_to):
    shutil.copy(file_path_from, file_path_to)

def copy_subset_of_kitti(input_folder_path, output_folder_path, idx_begin='000000', idx_end='001000'):
    # Improvements:

jogam5

import roboflow_datasets as roboflow
import os
import shutil

def make_dir(new_dir_path):
    if not os.path.exists(new_dir_path):
        os.makedirs(new_dir_path)
    print(f'{new_dir_path} created')

def copy_from_to(file_path_from, file_path_to):

jogam5

THIS WORKS!
import subprocess
cmd = ['tao', 'yolo_v4_tiny', 'export', '-m', 
       '/workspace/tao-experiments/yolo_v4_tiny/experiment_dir_retrain/weights/yolov4_cspdarknet_tiny_epoch_005.tlt', 
       '-k', 'YOUR_KEY', 
       '-e', '/workspace/tao-experiments/yolo_v4_tiny/specs/yolo_v4_tiny_retrain_kitti.txt', 
       '-o', '/workspace/tao-experiments/yolo_v4_tiny/export/yolov4_cspdarknet_tiny_epoch_005.etlt', 
       '--data_type', 'fp32', 
       '--gen_ds_config']

jogam5

# For the best DCGAN generator
# 1. covid small
# 2. rsna_10
# 3. rsna_50

noise = np.random.normal(0, 1, (100, 128))
gen_sample = generator.predict([noise, label]) # e.g. label is 0 or 1 in RSNA

# gen_sample in keras outputs (100, 64, 64, 3)
# e.g. for rsna_10 i need two vectors (100, 64, 64, 3), one for each lass

jogam5

# Note: 1 and 2 are simplifications to be more clear
# 1. Generate distribution: simplification
data_from_distribution = generate_rayleigh_CSI(parameter_1, parameter_2, parameter_3, ...)

# 2 Loop over data to compute wwmse: simplification
for sample in range(data_from_distribution):

    wwmse = WMMSE(number_of_transceivers, sample, max_power_budget, noise)
      # ==Parameters==:
      #

jogam5

package my.index;

import java.io.*;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;

jogam5

package my.midterm;

/*
'Overview' section provides a concise high level summary of Hadoop - MapReduce:
https://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html
 */

import java.io.*;
import java.util.*;
import org.apache.hadoop.fs.Path;

jogam5

Autodials (Github examples)
https://github.com/sloan58/twilio-autodialer
https://github.com/cristeab/autodialer

Build IVR with Lex
https://cogint.ai/conversational-ivr-with-amazons-lex-and-connect/

Amazon Lex Developer Resources
https://aws.amazon.com/lex/resources/
https://github.com/awslabs/aws-lex-browser-audio-capture

jogam5

# Option 1
go mod init
go build

# Option 2
export GO111MODULE=on
go mod init
go mod download
go mod vendor