coding brewery

Writing clean code is more of an art rather than a science. So What really makes code cleaner?. In this series called Clean Code Concepts, we investigate some of the ways to write code in a clean way. There are several aspects to write cleaner code, some are language agnostic others are language dependent. In this series, we would focus on some of the language-agnostic parts which can be used to improve your ability to write cleaner code in any language. So let's dive right into it. S.O.L.I.D is an acronym for the first five object-oriented design(OOD) principles by Robert C. Martin. It stands for: S - Single-responsibility principle O - Open-closed principle L - Liskov substitution principle I - Interface segregation principle D - Dependency Inversion Principle In this blog post, we would focus exclusively on Single-responsibility principle along with examples in Java and python. What is it? Single Responsibility Principle states that "A class should have one and only one reason to change." The question here is what begets change in software applications. It will be most probably due to the addition of new features(unless you are refactoring legacy code). How to think of SRP The best way to think about it is to think of each class as an API.  What does this class(or API) is intended to do? If we have to make modifications to a class/service, for different reasons, it means the abstraction is incorrect, and that the class has too many responsibilities. We want to avoid creating classes that try to "Do it All" Refactoring Code to SRP If you need to refactor some code and you want to check for SRP, you try to see if we can find methods that are mutually exclusive. In that case, we may want them to be part of separate classes. In other words, if we have 2 reasons to change for a class, we have to split the functionality into two classes. Implementation Suppose we need to create an Employee Management Software for an application. Below are the requirements Save and remove employees Print employee reports in XML and CSV Let's say we create a class with a UML like this:  Employee  id: long name: String dept: String working: Boolean  saveEmployee() removeEmployee() printXMLReport() printCSVReport() What is wrong with this approach The class is trying to do too much. It is saving to DB , preparing reports. Let’s say, nurses, doctors, etc inherit from the employee class. Now it will become a nightmare for programmers if every different employee object wants to handle the class in their own way. A single indentation change would require us to make code changes in a lot of places. Secondly, all the operations seem to be an operation that should be performed on an employee rather than the employee should perform these operations. Therefore these methods do not belong to employee class. What if the database implementation changes. Now we need to modify the code in the employee class and all classes that inherit it. Database interaction should be done by a separate class If the format of XML or CSV changes?. That would again lead to the same problem where an indentation change might lead to change in lots of places. Also, see do you see two mutually exclusive features here. The report formatting part of employee and data class of employee. Let's try to implement classes which follow SRP. Suppose we have

In statistics, a confidence interval (CI) is a type of interval estimate which we compute using the statistics of the observed data. The interval has an associated confidence level that, loosely speaking, quantifies the level of confidence that the value of the parameter lies in the interval. Let's say we have a population of data which contain weights of all students in a class. In this population, we can pick a random variable which is a sample(randomly selected) of n=10 students {x1,x2,...x10}. Now if we find the mean of the sample and compare it to mean of the population. We will see that the mean of the sample is very similar to mean of the population(not equal to(It may or may not be equal), but similar to). µ(sample) ≅ µ(population) In fact, as we increase the number of students in the sample (n), we see the mean of the sample becomes closer to mean of the population. Point Estimate vs Interval estimate

The question of confidence intervals (or interval estimates) comes in when we don't know the real mean value of the population and all we know is the mean of the sample. Here we can say with some degree of certainty/confidence/higher probability that the mean of the population is within a particular range. Example Let's say for our sample of n = 10 students, our mean of the sample is µ = 160 lbs and the standard deviation is σ=10 lbs. Now since we know that the distribution is Gaussian for weights of students in a general population, we can say The mean of the population lies between lbs with 95.4% probability. This estimation is called point estimate and the range is called confidence interval.

  Methods of finding confidence intervals   Case 1: if we know the standard deviation of the population. If σ(population) is known, we can say that if we have taken n samples, In that case, we can say,

In statistics, a confidence interval (CI) is a type of interval estimate which we compute using the statistics of the observed data. The interval has an associated confidence level that, loosely speaking, quantifies the level of confidence that the value of the parameter lies in the interval. Let's say we have a population of data which contain weights of all students in a class. In this population, we can pick a random variable which is a sample(randomly selected) of n=10 students {x1,x2,...x10}. Now if we find the mean of the sample and compare it to mean of the population. We will see that the mean of the sample is very similar to mean of the population(not equal to(It may or may not be equal), but similar to). µ(sample) ≅ µ(population) In fact, as we increase the number of students in the sample (n), we see the mean of the sample becomes closer to mean of the population. Point Estimate vs Interval estimate

The question of confidence intervals (or interval estimates) comes in when we don't know the real mean value of the population and all we know is the mean of the sample. Here we can say with some degree of certainty/confidence/higher probability that the mean of the population is within a particular range. Example Let's say for our sample of n = 10 students, our mean of the sample is µ = 160 lbs and the standard deviation is σ=10 lbs. Now since we know that the distribution is Gaussian for weights of students in a general population, we can say The mean of the population lies between lbs with 95.4% probability. This estimation is called point estimate and the range is called confidence interval.

  Methods of finding confidence intervals   Case 1: if we know the standard deviation of the population. If σ(population) is known, we can say that if we have taken n samples, In that case, we can say,

Suppose you have taken the data for heights and weights of students in class and you want to figure out the correlation between heights and weights of students. The relation between these two parameters is defined mathematically by one of the 3 ways 1) Covariance 2) Pearson Correlation Coefficient 3) Spearman's rank correlation coefficient Each of these metrics has its own pros and cons so let's dive deeper into them.   Covariance Covariance is a measure of the joint variability of two random variables. If the greater values of one variable mainly correspond with the greater values of the other variable, and the same holds for the lesser values, (i.e., the variables tend to show similar behavior), the covariance is positive. In the opposite case, when the greater values of one variable mainly correspond to the lesser values of the other, (i.e., the variables tend to show the opposite behavior), the covariance is negative. The sign of the covariance, therefore, shows the tendency in the linear relationship between the variables  

In case we want to the covariance of a variable with respect to itself, it is always zero.   A simple way to understand covariance is by using this graph as an example

In this we can see stock market returns increase as economic growth increases and vice versa, hence we can say these two are positively correlated. Further gasoline prices and world oil production decrease as the other increase and we can say they are negatively correlated. The reason why monotonically increasing seems to have positive covariance is because for any point they will be either above mean or below mean and hence make overall covariance +tive. Note 1) The magnitude of covariance has nothing to do with the amount of overlap. Let's say something has a covariance of 5 doesn't mean anything. In fact, even if we change the units of heights and weights from cms to feet, lbs to kgs the covariance for the same dataset will change. What if we standardize the datasets before applying covariance, that becomes correlation and that can tell how much the data is correlated. 2) However, if there are outliers in the dataset, we may have a situation where covariance is -time for monotonically increasing relation. Pearson correlation coefficient The Pearson correlation coefficient (PCC), also referred to as Pearson's r, the Pearson product-moment correlation coefficient (PPMCC) or the bivariate correlation, is a measure of the linear correlation between two variables X and Y. Owing to the Cauchy–Schwarz inequality it has a value between +1 and −1, where 1 is the total positive linear correlation, 0 is no linear correlation, and −1 is the total negative linear correlation

Suppose you have taken the data for heights and weights of students in class and you want to figure out the correlation between heights and weights of students. The relation between these two parameters is defined mathematically by one of the 3 ways 1) Covariance 2) Pearson Correlation Coefficient 3) Spearman's rank correlation coefficient Each of these metrics has its own pros and cons so let's dive deeper into them.   Covariance Covariance is a measure of the joint variability of two random variables. If the greater values of one variable mainly correspond with the greater values of the other variable, and the same holds for the lesser values, (i.e., the variables tend to show similar behavior), the covariance is positive. In the opposite case, when the greater values of one variable mainly correspond to the lesser values of the other, (i.e., the variables tend to show the opposite behavior), the covariance is negative. The sign of the covariance, therefore, shows the tendency in the linear relationship between the variables  

In case we want to the covariance of a variable with respect to itself, it is always zero.   A simple way to understand covariance is by using this graph as an example

In this we can see stock market returns increase as economic growth increases and vice versa, hence we can say these two are positively correlated. Further gasoline prices and world oil production decrease as the other increase and we can say they are negatively correlated. The reason why monotonically increasing seems to have positive covariance is because for any point they will be either above mean or below mean and hence make overall covariance +tive. Note 1) The magnitude of covariance has nothing to do with the amount of overlap. Let's say something has a covariance of 5 doesn't mean anything. In fact, even if we change the units of heights and weights from cms to feet, lbs to kgs the covariance for the same dataset will change. What if we standardize the datasets before applying covariance, that becomes correlation and that can tell how much the data is correlated. 2) However, if there are outliers in the dataset, we may have a situation where covariance is -time for monotonically increasing relation. Pearson correlation coefficient The Pearson correlation coefficient (PCC), also referred to as Pearson's r, the Pearson product-moment correlation coefficient (PPMCC) or the bivariate correlation, is a measure of the linear correlation between two variables X and Y. Owing to the Cauchy–Schwarz inequality it has a value between +1 and −1, where 1 is the total positive linear correlation, 0 is no linear correlation, and −1 is the total negative linear correlation