xor

I am using Oracle Java 7.51 on Ubuntu 12.04, and trying to do this long a = 0x0000000080000001 ^ 0x4065DE839A6F89EEL; System.out.println("result "+ Long.toHexString(a)); Output: result bf9a217c1a6f89ef But I was expecting result to be 4065de831a6f89ef , since ^ operator is a bitwise XOR in Java. Which part of Java specification am I reading wrong? You need an L at the end of the first integer literal: long a = 0x0000000080000001L ^ 0x4065DE839A6F89EEL; Otherwise it is an int literal, not a long (the leading zeroes being ignored). The ^ operator then promotes the first operand value from

I am calculating XOR of two short integers using XOR ^ operator in a traditional fashion. Below is the method- short a=197; short b=341; short y = (short) (a ^ b); However the XOR always returned integer but in my case inputs are short integer, that is why i am casting short to the XOR output. The XOR can be calculated in different manners (example: using BigInteger etc.) but performance wise (less time) which is the best for short integers? While keeping performance in mind, should i first convert each short integer to binary number using Integer.toBinaryString(number) then apply bitwise XOR?

I want to practice keras by code a xor, but the result is not right, the followed is my code, thanks for everybody to help me. from keras.models import Sequential from keras.layers.core import Dense,Activation from keras.optimizers import SGD import numpy as np model = Sequential()# two layers model.add(Dense(input_dim=2,output_dim=4,init="glorot_uniform")) model.add(Activation("sigmoid")) model.add(Dense(input_dim=4,output_dim=1,init="glorot_uniform")) model.add(Activation("sigmoid")) sgd = SGD(l2=0.0,lr=0.05, decay=1e-6, momentum=0.11, nesterov=True) model.compile(loss='mean_absolute_error',

I posted a question yesterday regarding issues that I was having with my backpropagating neural network for the XOR operator. I did a little more work and realized that it may have to do with not having a bias neuron. My question is, what is the role of the bias neuron in general, and what is its role in a backpropagating neural network that recognizes the XOR operator? Is it possible to create one without a bias neuron? Kiril It's possible to create a neural network without a bias neuron... it would work just fine, but for more information I would recommend you see the answers to this

I like the Python sum function : >>> z = [1] * 11 >>> zsum = sum(z) >>> zsum == 11 True I want the same functionality with using xor (^) not add (+). I want to use map. But I can not work out how to do this. Any hints? I am not satisfied with this : def xor(l): r = 0 for v in l: r ^= v return v I want a 1 liner using map. Hints? zxor = reduce(lambda a, b: a ^ b, z, 0) import operator zxor = reduce(operator.xor, z, 0) Note that starting Python 3.8 , and the introduction of assignment expressions (PEP 572) ( := operator), we can use and update a variable within a list comprehension and thus