RahulDas-dev

import numpy as np

# Frobenius norm (X) = Tr(X.X_T)  X_T = Transpose of X
# d(Frobenius norm)/dX = 2X


# M ~ L*R 
# R = l*r - M         
# Loss_fuction = NORM(R) to be minimized
# Loss_fuction = NORM(R) 

RahulDas-dev

import numpy as np

### Loss function : L(w₁,w₂) = 0.75(w₁ −2)² + 0.35(w₂−4)² 
### Gradient of Loss function : ∇ L = [0.75×2×(w₁ −2), 0.35×2×(w₂−4)]ᵀ
### Hyper Parameters : Learning Rate η =  0.1 , iteration or epoch N = 100

loss_f = lambda w1,w2: 0.75*(w1-2)**2 + 0.35*(w2-4)**2
grad_f = lambda w1,w2: np.array([1.5*(w1-2),0.7*(w2-4)])

eta, epoch = 0.1 ,100

RahulDas-dev

import argparse
import os

import pikepdf


perser = argparse.ArgumentParser()
perser.add_argument('Path', metavar='path', help='PDF directory')
args = perser.parse_args()

RahulDas-dev

RahulDas-dev

The Jupyter Notebook Installation for the Virtual Environment

Installation can be done pip install Jupyter. However big list dependency of Jupyter can be avoided by installting ipython kernel module only, if you have Jupyter installed Globally.

1. Install the ipython kernrl pip install ipykernel
2. Create a new kernel for your current virtual environment python -m ipykernel install --user --name=<promt>
3. Launch the notebook with jupyter notebbok from virtual Environment.
4. You should now be able to see your kernel in the IPython notebook menu: Kernel -> Change kernel

RahulDas-dev

def make_pipeline2(list_of_callables):
    def map_2_partials(item):
        func = item[0]
        #return func if len(item) < 2 else partial(func, **item[1])
        if len(item) < 2:
            return func 
        else :
            return partial(func, **item[1])
        
    list_of_partials = map(map_2_partials, list_of_callables)

RahulDas-dev

from functools import reduce

# creates pipeline object from list of callables
def make_pipeline(list_of_callables):
    return reduce(lambda f,g: lambda x: g(f(x)), list_of_callables)
  
add1 = lambda x: x+'1'  # Oprtaion 1 
add2 = lambda x: x+'2'  # Oprtaion 2
add3 = lambda x: x+'3'  # Oprtaion 3
add4 = lambda x: x+'4'  # Oprtaion 4 

RahulDas-dev

RahulDas-dev

import math
import pandas

# Loading data into pandas Dataframe
dataset = pd.read_csv(dataset_path)

print(f'dataset {dataset.shape}')
dataset.head()

#Shuffling the Dataset

RahulDas-dev

import math
from typing import List


def sliding_window(input_: List[int], kernel_: List[int]) -> List[int]:
    # Checking input and kernel length constraint
    if len(kernel_) > len(input_):
        raise ValueError("Input Size must be gratter than Kernel size")

    start_index = 0