'm new to mathematica. i have a small project: get an equation , a number n, a number x and a number y and a number h, then using euler formula calculate nTh iteration ... My code is :
f[x_,y_]=input["Please input f(x,y):"]
n=input["Please input number of iterations:"]
x0=input["Please input initial value x0:"]
y0=input["Please input initial value y0:"]
h=input["please input h:"]
For[i=0,i<n,i++,y0=y0+f[x0,y0]*h;x0=x0+h]
but when i copy this code in mathematica 9; it just print some texts and end . it did not get any input from user.just print this:
input["Please input f(x,y):"]
input["Please input number of iterations:"]
input["Please input initial value x0:"]
input["Please input initial value y0:"]
input["please input h:"]
and then end!
whould you help me ?
You should write it in two separate parts.
I would write an initialize variables part then the for loop.
Functions in Mathematica need a delayed equals := and to you recieved the outputs you did because Mathematica doesn't allow you to input while the code is running. you also should suppress the output with a ;
f[x_,y_]:= ; %%write in f(x,y)
n= ; %%input number of iterations
x0= ; %%input initial value x0
y0= ; %%input initial value y0
h= ; %%input h
An euler form of the solution is
euler:= Module[{ans, i, x, y, nsteps},
ans = {{x0,y0}};x=x0;y=y0;nsteps=n;
Do[(y=y+h*f[x,y];
x=x+h; ans=Append[and,{x,y}]),{i,1,nsteps}];ans]
To view the euler forumal result do...
eulerans1 = euler
Related
I'm fairly new to python so bare with me. I have plotted a histogram using some generated data. This data has many many points. I have defined it with the variable vals. I have then plotted a histogram with these values, though I have limited it so that only values between 104 and 155 are taken into account. This has been done as follows:
bin_heights, bin_edges = np.histogram(vals, range=[104, 155], bins=30)
bin_centres = (bin_edges[:-1] + bin_edges[1:])/2.
plt.errorbar(bin_centres, bin_heights, np.sqrt(bin_heights), fmt=',', capsize=2)
plt.xlabel("$m_{\gamma\gamma} (GeV)$")
plt.ylabel("Number of entries")
plt.show()
Giving the above plot:
My next step is to take into account values from vals which are less than 120. I have done this as follows:
background_data=[j for j in vals if j <= 120] #to avoid taking the signal bump, upper limit of 120 MeV set
I need to plot a curve on the same plot as the histogram, which follows the form B(x) = Ae^(-x/λ)
I then estimated a value of λ using the maximum likelihood estimator formula:
background_data=[j for j in vals if j <= 120] #to avoid taking the signal bump, upper limit of 120 MeV set
#print(background_data)
N_background=len(background_data)
print(N_background)
sigma_background_data=sum(background_data)
print(sigma_background_data)
lamb = (sigma_background_data)/(N_background) #maximum likelihood estimator for lambda
print('lambda estimate is', lamb)
where lamb = λ. I got a value of roughly lamb = 27.75, which I know is correct. I now need to get an estimate for A.
I have been advised to do this as follows:
Given a value of λ, find A by scaling the PDF to the data such that the area beneath
the scaled PDF has equal area to the data
I'm not quite sure what this means, or how I'd go about trying to do this. PDF means probability density function. I assume an integration will have to take place, so to get the area under the data (vals), I have done this:
data_area= integrate.cumtrapz(background_data, x=None, dx=1.0)
print(data_area)
plt.plot(background_data, data_area)
However, this gives me an error
ValueError: x and y must have same first dimension, but have shapes (981555,) and (981554,)
I'm not sure how to fix it. The end result should be something like:
See the cumtrapz docs:
Returns: ... If initial is None, the shape is such that the axis of integration has one less value than y. If initial is given, the shape is equal to that of y.
So you are either to pass an initial value like
data_area = integrate.cumtrapz(background_data, x=None, dx=1.0, initial = 0.0)
or discard the first value of the background_data:
plt.plot(background_data[1:], data_area)
According to Heron's formula for finding area of a triangle , if the sides of a triangle are a, b & c is :
s = (a+b+c) / 2
area =sqrt( s * (s-a) * (s-b) * (s-c)) # sqrt means square root
so for finding the area of the triangle using Heron's formula in Python, if I write code like this, will it be a valid practise? I have used assignment expression while calculating the area.
a = int(input("Enter value of first side")) # Assuming value is integer
b = int(input("Enter value of second side")) # Assuming value is integer
c = int(input("Enter value of third side")) # Assuming value is integer
area = ((s := (a+b+c) /2) *(s -a)*(s-b)*(s-c))**0.5
print("Area of the triangle is", area)
Yes, program counts correct. There is a one drawback in input: I recommend to add '\n' in input because it is uncomfortable to enter values without any space near text. In this code I fixed that. But you need to add checking if sides can make triangle.
a = int(input("Enter value of first side")) // Assuming value is integer
b = int(input("Enter value of second side")) // Assuming value is integer
c = int(input("Enter value of third side")) // Assuming value is integer
area = ((s := (a+b+c) /2) *(s -a)*(s-b)*(s-c))**0.5
print("Area of the triangle is", area)
I'm trying to set up a ampl model which clusters given points in a 2-dimensional space according to the model of Saglam et al(2005). For testing purposes I want to generate randomly some datapoints and then calculate the euclidian distance matrix for them (since I need this one). I'm aware that I could only make the distance matrix without the data points but in a later step the data points will be given and then I need to calculate the distances between each the points.
Below you'll find the code I've written so far. While loading the model I keep getting the error message "i is not defined". Since i is a subscript that should run over x1 and x1 is a parameter which is defined over the set D and have one subscript, I cannot figure out why this code should be invalid. As far as I understand, I don't have to define variables if I use them only as subscripts?
reset;
# parameters to define clustered
param m; # numbers of data points
param n; # numbers of clusters
# sets
set D := 1..m; #points to be clustered
set L := 1..n; #clusters
# randomly generate datapoints
param x1 {D} = Uniform(1,m);
param x2 {D} = Uniform(1,m);
param d {D,D} = sqrt((x1[i]-x1[j])^2 + (x2[i]-x2[j])^2);
# variables
var x {D, L} binary;
var D_l {L} >=0;
var D_max >= 0;
#minimization funcion
minimize max_clus_dis: D_max;
# constraints
subject to C1 {i in D, j in D, l in L}: D_l[l] >= d[i,j] * (x[i,l] + x[j,l] - 1);
subject to C2 {i in D}: sum{l in L} x[i,l] = 1;
subject to C3 {l in L}: D_max >= D_l[l];
So far I tried to change the line form param x1 to
param x1 {i in D, j in D} = ...
as well as
param d {x1, x2} = ...
Alas, nothing of this helped. So, any help someone can offer is deeply appreciated. I searched the web but I found nothing useful for my task.
I found eventually what was missing. The line in which I calculated the parameter d should be
param d {i in D, j in D} = sqrt((x1[i]-x1[j])^2 + (x2[i]-x2[j])^2);
Retrospectively it's clear that the subscripts i and j should have been mentioned on the line, I don't know how I could miss that.
I am trying to write an algorithm in python for knapsack problem. I did quite a few iterations and came to the following solution. It seems perfect for me. When I ran it on test sets,
it does not output optimal value
and sometimes gives maximum recursion depth error.
after changing the maxValues function it is outputting the optimal value, But it takes very very long time for datasets having more points. how to refine it
For the second problem, I have inspected the data for which it gives the error. The data is like huge and only just couple of them exceeds and the knapsack capacity. So it unnecessarily goes through the entire list.
So what I planned to do is at the start of running my recursive function, I tried to see the entire weights list where each weight is less than the current capacity and prune the rest. The following is the code I am planning to implement.
#~ weights_jump = find_indices(temp_w, lambda e: e < capacity)
#~ if(len(weights_jump)>0):
#~ temp_w[0:weights_jump[0]-1] = []
#~ temp_v[0:weights_jump[0]-1] = []
My main problem remains that why it is not outputting the optimal value. Please help me in this regards and also to integrate the above code into the current algorithm
The following is the main function. the input for this function is as follows,
A knapsack input contains n + 1 lines. The first line contains two integers, the first is the number
of items in the problem, n. The second number is the capacity of the knapsack, K. The remaining
lines present the data for each of the items. Each line, i ∈ 0 . . . n − 1 contains two integers, the
item’s value vi followed by its weight wi
eg input:
n K
v_0 w_0
v_1 w_1
...
v_n-1 w_n-1
def solveIt(inputData):
# parse the input
lines = inputData.split('\n')
firstLine = lines[0].split()
items = int(firstLine[0])
capacity = int(firstLine[1])
K = capacity
values = []
weights = []
for i in range(1, items+1):
line = lines[i]
parts = line.split()
values.append(int(parts[0]))
weights.append(int(parts[1]))
items = len(values)
#end of parsing
value = 0
weight = 0
print(weights)
print(values)
v = node(value,weights,values,K,0,taken);
# prepare the solution in the specified output format
outputData = str(v[0]) + ' ' + str(0) + '\n'
outputData += ' '.join(map(str, v[1]))
return outputData
The following is the recursive function
I'will try to explain this recursive function. Let's say I 'm starting off with root node and now I ill have two decisions to make either to take the first element or not.
before this I will call maxValue function to see the maximum value that can be obtained following this branch. If it is less than existing_max no need to search, so prune.
i will follow left branch if the weight of the first element is less than capacity. so append(1).
updating values,weights list etc and again call node function.
so it first transverses entire left branch and then transverses right branch.
in the right im just updating the values,weights lists and calling node function.
For this function inputs are
value -- The current value of the problem, It is initially set to zero and is it goes it gets increased
weights list
values list
current capacity
current max value found by algo. If this existing_max is greater than the maximum value that can be obtained by following a branch,
there is no need to search that branch. so entire branch is pruned
existing_nodes is the list which tells whether a particular item is taken (1) or not (0)
def node(value,weights,values,capacity,existing_max,existing_nodes):
v1=[];e1=[]; #values we get from left branch
v2=[];e2=[]; #values we get from right branch
e=[];
e = existing_nodes[:];
temp_w = weights[:]
temp_v = values[:];
#first check if the list is empty
if(len(values)==0):
r = [value,existing_nodes[:]]
return r;
#centre check if this entire branch could be pruned. it checks for max value that can be obtained is more than the max value inputted to this
max_value = value+maxValue(weights,values,capacity);
print('existing _max is '+str(existing_max))
print('weight in concern '+str(weights[0])+' value is '+str(value))
if(max_value<=existing_max):
return [0,[]];
#Transversing the left branch
#Transverse only if the weight does not exceed the capacity
print colored('leftbranch','red');
#search for indices of weights where weight < capacity
#~ weights_jump = find_indices(temp_w, lambda e: e < capacity)
#~ if(len(weights_jump)>0):
#~ temp_w[0:weights_jump[0]-1] = []
#~ temp_v[0:weights_jump[0]-1] = []
if(temp_w[0]<=capacity):
updated_value = temp_v[0]+value;
k = capacity-temp_w[0];
temp_w.pop(0);
temp_v.pop(0);
e1 =e[:]
e1.append(1);
print(str(updated_value)+' '+str(k)+' ')
raw_input('press ')
v1= node(updated_value,temp_w,temp_v,k,existing_max,e1);
#after transversing left node update existing_max
if(v1[0]>existing_max):
existing_max = v1[0];
else:
v1 = [0,[]]
#Transverse the right branch
#it implies we are not including the current value so remove that from weights and values.
print('rightbranch')
#~ print(str(value)+' '+str(capacity)+' ')
raw_input("Press Enter to continue...")
weights.pop(0);
values.pop(0);
e2 =e[:];
e2.append(0);
v2 = node(value,weights,values,capacity,existing_max,e2);
if(v1[0]>v2[0]):
return v1;
else:
return v2;
The following is the helper function maxValue which is called from recursive function
def maxValues(weights,values,K):
weights = weights;
values = values;
a=[];
l = 0;
#~ print('hello');
items = len(weights);
#~ print(items);
max = 0;k = K;
for i in range(0,items):
t = (i,float(values[i])/weights[i]);
a.append(t);
#~ print(i);
a = sorted(a,key=operator.itemgetter(1),reverse=True);
#~ print(a);
length = len(a);
for (i,v) in a:
#~ print('this is loop'+str(l)+'and k is '+str(k)+'weight is '+str(weights[i]) );
if weights[i]<=k:
max = max+values[i];
w = weights[i];
#~ print('this'+str(w));
k = k-w;
if(k==0):
break;
else:
max = max+ (float(k)/weights[i])*values[i];
w = k
k = k-w;
#~ print('this is w '+str(max));
if(k==0):
break;
l= l+1;
return max;
I spent days on this and could not do anything.
I've coded a basic Mandelbrot explorer in C#, but I have those horrible bands of color, and it's all greyscale.
I have the equation for smooth coloring:
mu = N + 1 - log (log |Z(N)|) / log 2
Where N is the escape count, and |Z(N)| is the modulus of the complex number after the value has escaped, it's this value which I'm unsure of.
My code is based off the pseudo code given on the wikipedia page: http://en.wikipedia.org/wiki/Mandelbrot_set#For_programmers
The complex number is represented by the real values x and y, using this method, how would I calculate the value of |Z(N)| ?
|Z(N)| means the distance to the origin, so you can calculate it via sqrt(x*x + y*y).
If you run into an error with the logarithm: Check the iterations before. If it's part of the Mandelbrot set (iteration = max_iteration), the first logarithm will result 0 and the second will raise an error.
So just add this snippet instead of your old return code. .
if (i < iterations)
{
return i + 1 - Math.Log(Math.Log(Math.Sqrt(x * x + y * y))) / Math.Log(2);
}
return i;
Later, you should divide i by the max_iterations and multiply it with 255. This will give you a nice rgb-value.