Dependency injection

dependency injection1.PNG

What is dependency injection?

Instead of initiating the object we are dependent on, we take help of a dependency framework which will push the ready made object to the dependent class during runtime.

In the above picture we are not initializing hotDrink with new hotDrink(), instead we are getting it as a constructor parameter.

Why is it useful?

This is useful to decouple two different code packages and remove direct dependency.

If you have an interface which can have multiple implementations, with the dependency injection framework, you can choose to run which implementation to run at runtime. Suppose you want to isolate and test a particular package, you can provide mock implementations of all other interfaces it is dependent on.

If you have to do the same without DI injection and interfaces, then you need to change code in lot of places, calling mock object at all the places you were calling original object to test.

It is useful when you have dependencies depending on other dependencies and so on.

Implementing with Google Guice:

Guice diGuice di2

You should usually initialize the injector where you bootstrap the program.

bind or guice configure() helps you define which implementation of interface to use.

@implementedby annotation can be used instead of configure() and bind.

@implementedby can be used as a default one.

If both guice module(bind) and @implementedby compete for different implementations of same interface, guice module wins.


Wanna have conditional logic to pick implementations?


Dependency injection without interface:

Assisted dependency injection: ##


when you want to select between implementation by giving type as input and also you want to pass input inside.


Design and Analysis of algorithms

The most important principle to be an algorithm designer is not to be content.

Asymptotic analysis:




The master method for analyzing the running time of divide and conquer algorithms:

  1. Black box solution for running time of you input few parameters related to the recurrence.
  2. Assumptions: All recurrences are of equal size.
  3. Master method

n- original problem size

a- number of recurrences called in each instance, rate at which subproblems proliferate. (Evil)

b- The factor by which the input size is divided for calling recurrences.

d- Polynomial exponent of the remaining work needed to merge solutions for the final solution.

b^d – Rate of work shrinkage per subproblem. (good)

Case 1; Same work at each level.

Case 2: More work at the root level.

Case 3: More work at leaf level.

Algorithm Design paradigms:

  1. Divide and conquer:
    1. write the base case.
    2. Using recursion to solve subproblems of a problem.
    3. Combine subproblems
    4. Store the results of subproblems in a hashmap and use them to trim other repeating recursive paths. (Dynamic programming)
  2. Randomization: Like in quick sort.
    1. Decomposition principle:
  3. Greedy algorithms.
  4. Dynamic programming.



n-num of vertices, m-num of edges

Graph partitioning: Cuts of a graph, Minimum cut


  1. Adjacency matrix. O(n^2)
  2. Adjacency lists. O(n+m)

Strongly connected components: Regions where you can go from any node A to any node B in the directed graph.

Kosaraju’s 2-pass strongly connected components algorithm:

One of the smartest and beautiful algorithms.

The structure of internet:

Structure of internet.PNG

Further reading: Networks, crowds and markets.



arcs – Directed edges, ordered pair.

Robust software development process

At scratch:

Document the requirements.

Divide and conquer: Break the implementation of the project into the small chunks of independent interacting software modules.

Design the proper architecture for these interacting modules.

Document the design.


  1. Build a module.
  2. write any simulators needed to give it input and see the output (optional).
  3. Test the module completely.

Repeat above three steps until all the modules are completed.

Put modules together in a proper interaction flow.

Write test cases and automate testing.


Read new requirements and add it to the requirements document to track or identify any reported scenarios as bugs or unexpected(requires new implementation).

Implement the requirement and test for implementation.

Do regression testing and update the regression test suite with the test cases of new implementation and modify the already existing test cases if necessary as per the requirement.

The process is complete.

Let’s talk about main elements of debugging for developer

  1. search (ctrl+f).
  2. breakpoints.
  3. call stack.
  4. call hierarchy.
  5. watch.
  6. data breakpoints and conditional breakpoints.
  7. logs.

Learn system design:


Sorting a list of tuples by multiple conditions:

sorted_by_length = sorted(list_,
                         key=lambda x: (x[0], len(x[1]), float(x[1])))

To get the lower bound integer in python:

int(1.6) =1



def function_name(i):


write a python file with extension and add definitions and statements in them.

‘import filename’ in another file.

access definitions inside that file using filename.nameofdef


concatenating to a tuple: tup=tup+(a[i],)

list1=[] – ordered sequence.


Can’t mutate a string. assignment error.

Aliasing problem in python:

if you have an object say a list and you attach it to the name list1

if you say list2=list1

list2=list1, then list2 points to same object as that of list one.

If you add an element to list2, then it will be reflected in list1 as well which you might not have intended.

So how to take a copy of list1 without having any impact on list1 if I do changes to the copy?

list2=list1[:]              cloned

sort the original list-> list1.sort()

take a sorted list of original list into another object

list2=sorted(list1) – [[1,2]]*3, all three lists inside outer list refer to same object. This doesn’t sync with your intuition at the beginning.

tuple and string doesn’t support item assignment i.e. s[1]=5, only list supports.

Functions as objects:

sending functions as arguments to other functions.

Higher order programming: Map

Dictionaries: A list of key, value pairs


‘Vivek’ in my_dict  -> True




Memorization: Storing the already computed values in dictionary and looking up for them in case we need them again. This will save from computing something that is already computed.


  1. Testing:
    1. Unit testing-
      1. validate each piece of the program.
      2. testing each function separately.
    2. Regression testing-
      1. After each fix, you need to retest to confirm already tested modules or not broken by the latest fix.
    3. Integration testing-
      1. Does overall program work?
    4. Black box testing-
      1. without looking at the code.
      2. done by other than the implementer to avoid some implementer biases.
    5. Glass box testing:
      1. Path complete.
      2. Design of test cases based on code.
  2. Defensive programming:
    1. specifications for functions.
    2. Modularize programs.
    3. check conditions on inputs/outputs.
  3. Eliminate the source of bugs:





raise ()

assert             to check whether the assumptions are met or not.

classes and objects:

Data attributes associated with the class, but not with instance or objects.

Generator: yield 1 in method. runs upto the yield and stops. again call the funct.__next__() runs upto next yield and stops.

Lisp and python programming languages allow you to implement reflection.

Using Pylab:

import pylab as plt

plt.figure(‘figurename’) – to plot in new figure




plt.xlabel(‘sample points’)

plt.ylabel(‘linear function’)


plt.plot(list of x values, list of y values,’b-‘,label=’linear’)


model=pylab.polyfit(observedX,observedY, n)

Finds coefficients of a polynomial of degree n, that provides a best least squares fit for the observed data. It returns n+1 values.



Anonymous function – lambda:

f1=lambda x,y: x+y


Credits: Jake Vanderplas

An abstract class in python can’t be instantiated.



using both 2.7 and 3.5 of python in diferent environments:

It won’t act weird if you will do it in tensor flow environment. Please follow these steps:
Install python 3.5

you can do the following to use python 3.5 on the new conda environnement “tensorflow”:

conda create –name tensorflow python=3.5
activate tensorflow
conda install jupyter
conda install scipy
pip install tensorflow
pip install tensorflow-gpu

You can uninstall python 2.7 also, but if you will follow above steps you can keep python 2.7 and python 3.5 also. They wont act weird. So in this case for anaconda by default it would be python 2.7. But for tensorflow environment it will work on python 3.5

view models of keras

import os
os.environ[“PATH”] += os.pathsep + ‘C:/Anaconda2/envs/tensorflow/Library/bin/graphviz’

from keras.utils.vis_utils import plot_model
plot_model(model, to_file=’model.png’)