I am getting an Attribute error on the main(). Can someone please help me out?
This is my class:
import math
class Cylinder2():
def __init__(self, cylRadius = 1, cylHeight = 1):
self.__radius = cylRadius
self.__height = cylHeight
def getPerimeter(self):
return (2 * math.pi * self.__radius)
def getEndArea(self):
return (math.pi * (self.__radius ** 2))
def getSideArea(self):
return (2 * (math.pi * (self.__radius * (self.__height))))
def getSurfaceArea(self):
return (2 * (math.pi * (self.__radius) * (self.__height + self.__radius)))
def getVolume(self):
return (math.pi * (self.__radius ** 2) * self.__height)
def setRadius(self, r):
self.__radius = r
def setHeight(self, h):
self.__height = h
def getRadius(self):
return self.__radius
def getHeight(self):
return self.__height
This is the main():
from CylinderModule2 import *
def main():
bottle = Cylinder2(4, 8)
bottle.setRadius(2)
bottle.setHeight(4)
print("The radius and height of the bottle are: ", bottle.__radius, " and ", bottle.__height)
print("The perimeter of the bottle end circle is", ("%.2f" % bottle.getPerimeter()))
print("The end circle area of the bottle is ", ("%.2f" % bottle.getEndArea()))
print("The side area of the bottle is ", ("%.2f" % bottle.getSideArea()))
print("The total surface of the bottle is ", ("%.2f" % bottle.getSurfaceArea()))
print("The volume of the bottle is ", ("%.2f" % bottle.getVolume()))
main()
This is the error I get:
print("The radius and height of the bottle are: ", bottle._radius, " and ", bottle._height)
AttributeError: 'Cylinder2' object has no attribute '__radius'
bottle.__radius and bottle.__height are what can loosely be described as private variables (even though they are not really, you can read about what I mean here: http://docs.python.org/2/tutorial/classes.html#private-variables-and-class-local-references )
"private" variables can not be directly accessed from outside of the class. In Python, they are denoted by using two underscores (as in __radius).
So what you want to do is using the getters, which are bottle.getRadius() and bottle.getHeight() instead.
Related
I am trying to explore Scala. I am new to Scala. This might be a simple question and searched in google to get below scenario to solve. But couldn't get answers. Instead of Scala I am getting Java related things.
My requirement to print format like below.
* * * * *
* * * *
* * *
*
Can someone suggest me how to get this format.
Thanks in advance.
Kanti
Just for the sake of illustration, here are two possible solution to the problem.
The first one is completely imperative, while the second one is more functional.
The idea is that this serves as an example to help you think how to solve problems in a programmatic way.
As many of us have already commented, if you do not understand the basic ideas behind the solution, then this code will be useless in the long term.
Here is the imperative solution, the idea is simple, we need to print n lines, each line contains n - i starts (where i is the number of the line, starting at 0). The starts are separated by an empty space.
Finally, before printing the starts, we need some padding, looking at example inputs, you can see that the padding starts at 0 and increases by 1 for each line.
def printReverseTriangle(n: Int): Unit = {
var i = 0
var padding = 0
while (i < n) {
var j = padding
while (j > 0) {
print(" ")
j -= 1
}
var k = n - i
while (k > 0) {
print("* ")
k -= 1
}
println()
i += 1
padding += 1
}
}
And here is a more functional approach.
As you can see, in this case we do not need to mutate anything, all the high level operators do that for us. And we only need to focus on the description of the solution.
def printReverseTriangle(size: Int): Unit = {
def makeReverseTriangle(size: Int): List[String] =
List.tabulate(size) { i =>
(" " * (size - i)) + ("* " * i)
}.reverse
println(makeReverseTriangle(size).mkString("\n"))
}
To add an alternative to Luis's answer, here's a recursive solution:
import scala.annotation.tailrec
def printStars(i: Int): Unit = {
#tailrec
def loop(j: Int): Unit = {
if(j > 0) {
val stars = Range(0, j).map(_ => "*").mkString(" ") // make stars
if(i == j) println(stars) // no need for spaces
else println((" " * (i - j)) + stars) // spaces before the stars
loop(j - 1)
}
}
loop(i)
}
printStars(3)
// * * *
// * *
// *
This function will take a maximum triangle size (i), and for that size until i is no longer greater than 0 it will print out the correct number of stars (and spaces), then decrement by 1.
Note: Range(0, j).map(_ => "*").mkString(" ") can be replaced with List.tabulate(j)(_ => "*").mkString(" ") per Luis's answer - I'm not sure which is faster (I've not tested it).
I’m trying to implement Adam by myself for a learning purpose.
Here is my Adam implementation:
class ADAMOptimizer(Optimizer):
"""
implements ADAM Algorithm, as a preceding step.
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.99), eps=1e-8, weight_decay=0):
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
super(ADAMOptimizer, self).__init__(params, defaults)
def step(self):
"""
Performs a single optimization step.
"""
loss = None
for group in self.param_groups:
#print(group.keys())
#print (self.param_groups[0]['params'][0].size()), First param (W) size: torch.Size([10, 784])
#print (self.param_groups[0]['params'][1].size()), Second param(b) size: torch.Size([10])
for p in group['params']:
grad = p.grad.data
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Momentum (Exponential MA of gradients)
state['exp_avg'] = torch.zeros_like(p.data)
#print(p.data.size())
# RMS Prop componenet. (Exponential MA of squared gradients). Denominator.
state['exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
b1, b2 = group['betas']
state['step'] += 1
# L2 penalty. Gotta add to Gradient as well.
if group['weight_decay'] != 0:
grad = grad.add(group['weight_decay'], p.data)
# Momentum
exp_avg = torch.mul(exp_avg, b1) + (1 - b1)*grad
# RMS
exp_avg_sq = torch.mul(exp_avg_sq, b2) + (1-b2)*(grad*grad)
denom = exp_avg_sq.sqrt() + group['eps']
bias_correction1 = 1 / (1 - b1 ** state['step'])
bias_correction2 = 1 / (1 - b2 ** state['step'])
adapted_learning_rate = group['lr'] * bias_correction1 / math.sqrt(bias_correction2)
p.data = p.data - adapted_learning_rate * exp_avg / denom
if state['step'] % 10000 ==0:
print ("group:", group)
print("p: ",p)
print("p.data: ", p.data) # W = p.data
return loss
I think I implemented everything correct however the loss graph of my implementation is very spiky compared to that of torch.optim.Adam.
My ADAM implementation loss graph (below)
torch.optim.Adam loss graph (below)
If someone could tell me what I am doing wrong, I’ll be very grateful.
For the full code including data, graph (super easy to run): https://github.com/byorxyz/AMS_pytorch/blob/master/AdamFails_1dConvex.ipynb
I found a code online for Naive bayes classification for a small research I am doing. The code I am using is showing some errors and cannot find the solution for them. I would greatly appreciate your help.
The code is below:
# Example of Naive Bayes implemented from Scratch in Python
import csv
import random
import math
def loadCsv(filename):
lines = csv.reader(open(filename, "rt"))
dataset = list(lines)
for i in range(len(dataset)):
dataset[i] = [float(x) for x in dataset[i]]
return dataset
def splitDataset(dataset, splitRatio):
trainSize = int(len(dataset) * splitRatio)
trainSet = []
copy = list(dataset)
while len(trainSet) < trainSize:
index = random.randrange(len(copy))
trainSet.append(copy.pop(index))
return [trainSet, copy]
def separateByClass(dataset):
separated = {}
for i in range(len(dataset)):
vector = dataset[i]
if (vector[-1] not in separated):
separated[vector[-1]] = []
separated[vector[-1]].append(vector)
return separated
def mean(numbers):
return sum(numbers) / float(len(numbers))
def stdev(numbers):
avg = mean(numbers)
variance = sum([pow(x - avg, 2) for x in numbers]) / float(len(numbers) - 1)
return math.sqrt(variance)
def summarize(dataset):
summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)]
del summaries[-1]
return summaries
def summarizeByClass(dataset):
separated = separateByClass(dataset)
summaries = {}
for classValue, instances in separated.items():
summaries[classValue] = summarize(instances)
return summaries
def calculateProbability(x, mean, stdev):
exponent = math.exp(-(math.pow(x - mean, 2) / (2 * math.pow(stdev, 2))))
return (1 / (math.sqrt(2 * math.pi) * stdev)) * exponent
def calculateClassProbabilities(summaries, inputVector):
probabilities = {}
for classValue, classSummaries in summaries.items():
probabilities[classValue] = 1
for i in range(len(classSummaries)):
mean, stdev = classSummaries[i]
x = inputVector[i]
probabilities[classValue] *= calculateProbability(x, mean, stdev)
return probabilities
def predict(summaries, inputVector):
probabilities = calculateClassProbabilities(summaries, inputVector)
bestLabel, bestProb = None, -1
for classValue, probability in probabilities.items():
if bestLabel is None or probability > bestProb:
bestProb = probability
bestLabel = classValue
return bestLabel
def getPredictions(summaries, testSet):
predictions = []
for i in range(len(testSet)):
result = predict(summaries, testSet[i])
predictions.append(result)
return predictions
def getAccuracy(testSet, predictions):
correct = 0
for i in range(len(testSet)):
if testSet[i][-1] == predictions[i]:
correct += 1
return (correct / float(len(testSet))) * 100.0
def main():
filename = 'E:\iris.data.csv'
splitRatio = 0.67
dataset = loadCsv(filename)
trainingSet, testSet = splitDataset(dataset, splitRatio)
print(('Split {0} rows into train={1} and test={2} rows').format(len(dataset), len(trainingSet), len(testSet)))
# prepare model
summaries = summarizeByClass(trainingSet)
# test model
predictions = getPredictions(summaries, testSet)
accuracy = getAccuracy(testSet, predictions)
print(('Accuracy: {0}%').format(accuracy))
main()
The traceback for the same is below:
File "<ipython-input-18-4397d9969e66>", line 1, in <module>
runfile('C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py', wdir='C:/Users/Lenovo/Desktop/EE Codes')
File "C:\Users\Lenovo\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile
execfile(filename, namespace)
File "C:\Users\Lenovo\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 101, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 76, in <module>
main()
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 69, in main
neighbors = getNeighbors(trainingSet, testSet[x], k)
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 31, in getNeighbors
dist = euclideanDistance(testInstance, trainingSet[x], length)
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 24, in euclideanDistance
distance += pow((instance1[x] - instance2[x]), 2)
TypeError: unsupported operand type(s) for -: 'str' and 'str'
I would request you all to please provide a solution to how to solve this error for the respective code. If you require the dataset then please do ask. I can provide you the link for that too.
Thanks in advance
There is a new class. Each instance contain a refernce to next instance.
The goal is to build __repr__(self) function, that represent element of the class with all referenced elements, when each value wrapped by defined symbol. For example by # or *.
Something like this:
****** ****** ******
* 10 * * 20 * * 30 *
****** ****** ******
There is an example:
class Node:
def __init__(self, value):
self.value = value
self.pointer = None
#pointer.setter
def pointer(self, next_node):
self.pointer = next_node
p1 = Node(10)
p2 = Node(20)
p3 = Node(30)
p1.pointer = p2
p2.pointer = p3
print (p1)
That what I did:
def __str__(self):
ans = "{1} {0} {1}".format(self.value, "*")
wrap = "{0:{1}^{2}}".format("", "*", len(ans))
return "{1}\n* {0} *\n{1}".format(ans, wrap)
But it works only for one element.
For printing of all referenced elements i wrote another function:
def show(self):
buf = self
while buf.value:
print(buf)
if buf.pointer:
buf = buf.pointer
else:
break
But it prints each element on a new line. Like this:
******
* 10 *
******
******
* 20 *
******
******
* 30 *
******
The problem is that i don't know how return on first line with some indent and print the next 3 lines elemen.
The question:
Is there some way to build __repr__() function for representing all referenced elements?
That's I have done... Forgive me for this huge code. I am just pretty new in python.
I solved it by .format() funtion. And by printing 3 lines at the output. But i still wait and hope for more elegant solution...
There is also option to change the wrapping character in print_linked_list fuction. And it prints only 5 following values.
def __repr__(self):
return self.print_linked_list()
__str__ = __repr__
def print_linked_list(self, border="*", count=5):
# examples of printing:
# ***** ****** ******* ****** *****
# * 3 * -> * 80 * -> * 100 * -> * 20 * -> * 8 *
# ***** ****** ******* ****** *****
ar = " -> "
wrap, ans_string = str(), str()
buf = self
while buf and count >= 0:
ans = "{0} {1} {0}".format(border, buf.value)
ans_string += ans + (ar if buf.next else "")
wrap += "{0:{1}<{2}}".format("", border, len(ans)) + \
"{:^{}}".format("", len(ar))
buf = buf.next if buf.next else False
count -= 1
return "\n{0}\n{1}\n{0}".format(wrap, ans_string)
What I'm trying to do is take numerous(up to 48) 1024x768 images that are color coded images(weather maps, the precip overlay) and add up the precip to fall over the course of time. When I run into non-precip I want to take a box 5x5 around the pixel in question and average the value and use that value as the value of the pixel in question.
I can do this but it takes a long time to accomplish it. I have heard numpy could improve the speed but I still haven't been able to wrap my mind around how it going to improve the speed given the sequence of events that have to take place. It seems like I would still have to do it pixel by pixel. I've included an idea of the code I'm using to accomplish this SLOWLY.
I have this actually as two separate program, one to download the images and the other does the image processing(working up toward merging the two programs in the near future, just trying to get all the bugs worked out before the merger.) Hence some of the download coding may look a little strange. I figure I could probably write the file straight to a variable but I haven't been doing it that way so I stuck with a bit longer approach.
Is there anyway of increasing the speed? I don't see anyway of avoiding pixel by pixel due to the color coding scheme in place(look at the color bar in the lower left it shows the full color scheme...I only included part of it for demo purposes in the coding below.) Some of the coding may be a bit rough since I chopped from the two programs and put the important parts in here...it shows what I'm currently doing and gives the full idea of how I'm going about doing it.
Also, if you happen to see this three to four or more days after it was posted you would need to change the date in the download link to the current date. The files are only kept on the server for 3-4 days before they are removed.
from PIL import Image
import time
import urllib
import os
pathstr = '/'
url = 'http://mag.ncep.noaa.gov/GemPakTier/MagGemPakImages/gfs/20140216/00/gfs_namer_006_1000_500_thick.gif'
urllib.urlretrieve(url,str(pathstr + '20140216006.gif'))
url = 'http://mag.ncep.noaa.gov/GemPakTier/MagGemPakImages/gfs/20140216/00/gfs_namer_012_1000_500_thick.gif'
urllib.urlretrieve(url,str(pathstr + '20140216012.gif'))
url = 'http://mag.ncep.noaa.gov/GemPakTier/MagGemPakImages/gfs/20140216/00/gfs_namer_018_1000_500_thick.gif'
urllib.urlretrieve(url,str(pathstr + '20140216018.gif'))
url = 'http://mag.ncep.noaa.gov/GemPakTier/MagGemPakImages/gfs/20140216/00/gfs_namer_024_1000_500_thick.gif'
urllib.urlretrieve(url,str(pathstr + '20140216024.gif'))
class Convert():
def __init__(self):
self.colorscale2 = [(255,255,255),(127,255,0),(0,205,0),(145,44,238),(16,78,139),
(30,144,255),(0,178,238),(0,238,238),(137,104,205),(0,139,0),
(139,0,139),(139,0,0),(205,0,0),(238,64,0),(255,127,0),(205,133,0),
(255,215,0),(238,238,0),(255,255,0),(139,71,38),(255,0,0),(0,0,255),(0,0,0)]
self.x = 0
self.y = 0
self.grid = 0
self.moist = 0
self.scan = 0
self.turn = 0
self.precip = {}
start = time.time()
for i in range(6, 30, 6):
if i < 10:
filename = '/2014021600' + str(i) + '.gif'
else:
filename = '/201402160' + str(i) + '.gif'
self.im1 = Image.open(filename).convert('RGB')
self.image = self.im1.getdata()
self.size = width, height = self.im1.size
self.coordinates = self.x,self.y = width, height
self.getprecip()
self.turn = 1
print (time.time()-start)
def getprecip(self):
for self.x in range(81, 950):
for self.y in range(29, 749):
if self.turn == 0:
self.moist = 0
else:
self.moist = self.precip[self.x,self.y]
self.coordinates = self.x,self.y
self.scan = 0
self.imagescan()
if self.turn == 0:
self.precip[self.x,self.y] = self.moist
else:
self.precip[self.x,self.y] += self.moist
def imagescan(self):
if self.image[(self.y * 1024) + self.x] == self.colorscale2[0]:
self.moist =0
self.grid -=1
elif self.image[(self.y * 1024) + self.x] == self.colorscale2[1]:
self.moist =.01
elif self.image[(self.y * 1024) + self.x] == self.colorscale2[2]:
self.moist =.1
elif self.image[(self.y * 1024) + self.x] == self.colorscale2[3]:
self.moist =.25
elif self.image[(self.y * 1024) + self.x] == self.colorscale2[4]:
self.moist =.5
#on and on through self.colorscale2[18]
if self.scan == 1:
self.grid += 1
if self.scan == 0:
x = self.x
y = self.y
self.deliso540()
self.x = x
self.y = y
def deliso540(self):
self.grid = 1
self.scan = 1
for p in range(self.x-2,self.x+2):
for q in range(self.y-2,self.y+2):
self.x = p
self.y = q
self.imagescan()
self.moist = self.moist / self.grid