This article comprehensively covers the different logic gates in Python. Logic gates are the most basic materials to implement digital components. The use of logic gates ranges from computer architecture to the field of electronics.
These gates deal with binary values, either 0 or 1. Different types of gates take different numbers of input, but all of them provide a single output. These logic gates when combined form complicated circuits. This gate provides the output as 1 if either of the inputs is 1. The table displayed above is the truth table. It is used to showcase all the combinations of values for inputs to a 'OR' gate. The figure alongside the table denotes an 'OR' gate. This gate provides an output of 0 if either of the inputs are 0.
This operation is considered as multiplication in binary numbers. We can see in the truth table that whenever either of the two inputs is 0, the output is 0 too. The alongside figure denotes the 'AND' gate. The above table clearly displays the reversal of bits. The adjoining figure represents the 'NOT' gate. Note: The 'NOT ' function provides correct results for bit values 0 and 1. They are named universal because any boolean circuit can be implemented using only these gates.
Opposite to 'AND' gate, it provides an output of 0 only when both the bits are set, otherwise 1. Contrary to 'OR' gate, it provides an output of 1, when all the inputs are 0. We can clearly see the output for the XOR Gate in the table above. It provides an output of 1 when the number of ones in the input is 1, that is odd.
Opposite to 'XOR'it provides an output of 1, when the number of 1s in the input is even. The implementation of logic gates in Python is very easy.
As a programmer, you need to be aware of logic gates and operators in Python. We hope that this article enlightened the reader about the basics and execution of logic gates in Python.An XOR gate implements an exclusive or ; that is, a true output results if one, and only one, of the inputs to the gate is true.
XOR represents the inequality function, i. A way to remember XOR is "must have one or the other but not both". XOR can also be viewed as addition modulo 2.
As a result, XOR gates are used to implement binary addition in computers. Other uses include subtractors, comparators, and controlled inverters. The behavior of XOR is summarized in the truth table shown on the right. For more information see Logic Gate Symbols.
Note that the caret does not denote logical conjunction AND in these languages, despite the similarity of symbol. Here is a diagram of a pass transistor logic implementation of an XOR gate. Note: The "Rss" resistor prevents shunting current directly from "A" and "B" to the output.
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Without it, if the circuit that provides inputs A and B does not have the proper driving capability, the output might not swing rail to rail or be severely slew-rate limited. The "Rss" resistor also limits the current from Vdd to ground which protects the transistors and saves energy when the transistors are transitioning between states.
If a specific type of gate is not available, a circuit that implements the same function can be constructed from other available gates. However, this approach requires five gates of three different kinds. For the NAND constructions, the upper arrangement requires fewer gates.
For the NOR constructions, the lower arrangement offers the advantage of a shorter propagation delay the time delay between an input changing and the output changing. Literal interpretation of the name "exclusive or", or observation of the IEC rectangular symbol, raises the question of correct behaviour with additional inputs.
If a logic gate were to accept three or more inputs and produce a true output if exactly one of those inputs were true, then it would in effect be a one-hot detector and indeed this is the case for only two inputs.
However, it is rarely implemented this way in practice. It is most common to regard subsequent inputs as being applied through a cascade of binary exclusive-or operations: the first two signals are fed into an XOR gate, then the output of that gate is fed into a second XOR gate together with the third signal, and so on for any remaining signals.
The result is a circuit that outputs a 1 when the number of 1s at its inputs is odd, and a 0 when the number of incoming 1s is even.Computers seem to be everywhere around us these days. Despite that, few people understand how they actually work.
To have a decent understanding, one must first understand logic gates since they are the building blocks of any computer system. This article will help you understand logic gates, and it will also help you learn how to simulate them in Python so that you can experiment with them.
For each gate, we will create a Python function that will represent it and mimic its functionality. The AND gate is often represented with the symbol above. In Python, the word and is a reserved keyword, therefore this function will be called AND, and to be consistent, we will use all-caps for all of our gate functions.
Another reason or doing this is because gate names are usually written in all-caps in computer science. This helps to clarify that they are gates since some of them use common words for their names. Style note: The Python Style Guide PEP-8 recommends lower-case words for function names, and it also recommends for those words to be separated by underscores when more than one word is used. However, the code below is actually a better representation of how an AND gate actually functions, so we will use it instead.
It may seem unnecessary to make the code longer than it needs to be, but hopefully writing it this way will help give you a better understanding of AND gates outside of this simulation. Plus, it seems cheap to use the and keyword inside of our AND function.
Just as the case was with andor is also a reserved keyword in Python. Our OR function will also require two inputs and return one Boolean output.
The function will return True if either input is True. It will also return True if they are Both True. It will return False when neither of the inputs are True.
The code directly below shows a simple way of writing this function:. However, just as the case was with our AND function, there is a way to write it that better represents an OR gate outside of our program:. The NOT gate is relatively simple. For our NOT function, we will just accept one Boolean input and return its opposite.
In other words, True inputs will become False outputs and vice versa. The NOT gate can be combined with either of the previous two types of gates.
Defining these new functions is easy since we have already created the three gates that will be used in the two next gates. It is important to know that order of precedence matters when using logical gates. One must be careful to not use our functions incorrectly. The XOR exclusive or gate is more complicated than the others mentioned previously, but the difficulty of writing the code has been significantly reduced by the fact that we have already created our AND, OR, and NOT functions.
The code to implement it is as follows:. As you can see, what this allows us to do is to return a True value when one of the inputs is True, but not when both are True as the case is with the OR gate. In other words, it returns True when the Boolean inputs differ, but not when they are the same.
This is a very powerful tool because it can be used alongside an AND gate to allow the addition of binary numbers. If you think of the Boolean value True as the number 1, and the Boolean value False as 0, you may realize that the result of the XOR functions correspond to the addition problems.
This is where the AND gate is useful. Since AND True, True returns True, and since using any other combination of True or False inputs in an AND gate returns False, we can use it to determine whether or not there is an extra 1 to carry over to the next decimal place.
We can create another function that can perform this addition for us. This is referred to as a half-adder. You probably noticed that this function returns tuples containing two values.For example, I have two variables that I expect to be strings.
I want to test that only one of them contains a True value is not None or the empty string :. But this is a little too verbose for me, and isn't particularly clear at first glance. Another way to do it is:. The xor operator on two booleans is logical xor unlike on ints, where it's bitwise. If you're already normalizing the inputs to Hey, Roshin, I would like to give you Assuming that your file unique. You can also use the random library's You can simply the built-in function in Already have an account?
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For example, I have two variables that I expect to be strings. I want to test that only one of them contains a True value is not None or the empty string :. But this is a little too verbose for me, and isn't particularly clear at first glance. Another way to do it is:. The xor operator on two booleans is logical xor unlike on ints, where it's bitwise.
Implementing the XOR Gate using Backpropagation in Neural Networks
Which makes sense, since bool is just a subclass of intbut is implemented to only have the values 0 and 1. And logical xor is equivalent to bitwise xor when the domain is restricted to 0 and 1. Credit to Nick Coghlan on the Python mailing list. As Zach explained, you can use:.
Edit: Examples 1 and 3 above are missing parenthes so result is incorrect. Anoder idea However; as they indicated above, it depends of the actual behavior you want to pull out about any couple strings, because strings aren't boleans If an exclusive choice is what you're after, i. As I don't see the simple variant of xor using variable arguments and only operation on Truth values True or False, I'll just throw it here for anyone to use.
It's as noted by others, pretty not to say very straightforward. As this is the generalized n-ary logical XOR, it's truth value will be True whenever the number of True operands is odd and not only when exactly one is True, this is just one case in which n-ary XOR is True.
Thus if you are in search of a n-ary predicate that is only True when exactly one of it's operands is, you might want to use:. I know this is late, but I had a thought and it might be worth, just for documentation. Perhaps this would work: np. Sometimes I find myself working with 1 and 0 instead of boolean True and False values.
In this case xor can be defined as. Some of the implementations suggested here will cause repeated evaluation of the operands in some cases, which may lead to unintended side effects and therefore must be avoided. That said, a xor implementation that returns either True or False is fairly simple; one that returns one of the operands, if possible, is much trickier, because no consensus exists as to which operand should be the chosen one, especially when there are more than two operands.
For instance, should xor None, -1, , True return None or False? I bet each answer appears to some people as the most intuitive one. For either the True- or the False-result, there are as many as five possible choices: return first operand if it matches end result in value, else booleanreturn first match if at least one exists, else booleanreturn last operand if Many folks, including myself, need an xor function that behaves like an n-input xor circuit, where n is variable.
The following simple function implements this.The Perceptron Model implements the following function:. For a particular choice of the weight vector and bias parameterthe model predicts output for the corresponding input vector. XOR logical function truth table for 2-bit binary variablesi. We can observe that, Designing the Perceptron Network:.
Logic Gates in Python
For the implementation, the weight parameters are considered to be and the bias parameters are. Here, the model predicted output for each of the test inputs are exactly matched with the XOR logic gate conventional output according to the truth table.
Hence, it is verified that the perceptron algorithm for XOR logic gate is correctly implemented. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute. See your article appearing on the GeeksforGeeks main page and help other Geeks. Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.
This neural network will deal with the XOR logic problem. An XOR exclusive OR gate is a digital logic gate that gives a true output only when both its inputs differ from each other. The truth table for an XOR gate is shown below:. The goal of the neural network is to classify the input patterns according to the above truth table. If the input patterns are plotted according to their outputs, it is seen that these points are not linearly separable.
Hence the neural network has to be modeled to separate these input patterns using decision planes. As mentioned before, the neural network needs to produce two different decision planes to linearly separate the input data based on the output patterns. This is achieved by using the concept of hidden layers. The neural network will consist of one input layer with two nodes X1,X2 ; one hidden layer with two nodes since two decision planes are needed ; and one output layer with one node Y.
Hence, the neural network looks like this:. The characteristics of a Sigmoid Neuron are:. Can accept real values as input. The value of the activation is equal to the weighted sum of its inputs i. The output of the sigmoid neuron is a function of the sigmoid function, which is also known as a logistic regression function.
The sigmoid function is a continuous function which outputs values between 0 and The information of a neural network is stored in the interconnections between the neurons i. A neural network learns by updating its weights according to a learning algorithm that helps it converge to the expected output. The learning algorithm is a principled way of changing the weights and biases based on the loss function.
This is a fairly simple learning algorithm consisting of only arithmetic operations to update the weights and biases. Step 1: To initialize the weights and biases with random values. The forward pass involves compute the predicted output, which is a function of the weighted sum of the inputs given to the neurons:.
Based on this comparison, the weights for both the hidden layers and the output layers are changed using backpropagation. Backpropagation is done using the Gradient Descent algorithm. The loss function of the sigmoid neuron is the squared error loss.
Our goal is to find the weight vector corresponding to the point where the error is minimum i.