Here the variable V4_15 is corralated to V4_15_0, V4_15_1, V4_15_2, V4_15_3, V4_15_4, V4_15_5, V4_15_6, V4_15_7.
If those later variables are 0 then V4_15 is 0 (Absent) and if any of those variables is 1 then V4_15 is 1 (present).
Any idea how can I do this using Transform > Compute variable?
I am a beginner level user.
Put this in a new syntax window and run it:
compute V4_15 = any(1, V4_15_0 to V4_15_7).
execute.
In general I recommend learning to use syntax - after a bit of getting used to it will get you much further, much quicker.
Related
I am trying to restrict the number of threads that TensorFlow spawns. In python, I understand we need to use the following steps as pointed out Here. I was trying to do the same in CPP, but it doesn't seem that straight forward.
Questions:
How to modify intra_op_parallelism_threads and inter_op_parallelism_threads correctly?
How to modify the device_count to control the core as well?
SessionOptions options;
ConfigProto* config = &options.config;
string key = "CPU";
//not sure if this is the correct way to do it.
(*config->mutable_device_count())[key] = 1;
config->set_inter_op_parallelism_threads(1);
config->set_intra_op_parallelism_threads(1);
Answer to 1 as Fisa pointed out is correct. With just minor adjustment as config is a pointer.
SessionOptions options;
ConfigProto* config = &options.config;
//single thread control//
config->set_inter_op_parallelism_threads(1);
config->set_intra_op_parallelism_threads(1);
fSession.reset(NewSession(options));
Answer to question 1:
tensorflow::SessionOptions options;
tensorflow::ConfigProto & config = options.config;
config.set_inter_op_parallelism_threads(1);
config.set_intra_op_parallelism_threads(1);
session->reset(tensorflow::NewSession(options));
This will reduce the total number of threads (but not to 1) generated by TensorFlow. The total number of threads generated by TensorFlow will still be multiple, depending on the number of cores in the CPU. In most of the cases, only one thread will be active while others will be in sleeping mode. I don't think it is possible to have a single-threaded TensorFlow.
The following github issues support my point of view.
https://github.com/tensorflow/tensorflow/issues/33627
https://github.com/usnistgov/frvt/issues/30
I am new to Python, coming from MATLAB, and long ago from C. I have written a script in MATLAB which simulates sediment transport in rivers as a Markov Process. The code randomly places circles of a random diameter within a rectangular area of a specified dimension. The circles are non-uniform is size, drawn randomly from a specified range of sizes. I do not know how many times I will step through the circle placement operation so I use a while loop to complete the process. In an attempt to be more community oriented, I am translating the MATLAB script to Python. I used the online tool OMPC to get started, and have been working through it manually from the auto-translated version (was not that helpful, which is not surprising). To debug the code as I go, I use the
MATLAB generated results to generally compare and contrast against results in Python. It seems clear to me that I have declared variables in a way that introduces problems as calculations proceed in the script. Here are two examples of consistent problems between different instances of code execution. First, the code generated what I think are arrays within arrays because the script is returning results which look like:
array([[ True]
[False]], dtype=bool)
This result was generated for the following code snippet at the overlap_logix operation:
CenterCoord_Array = np.asarray(CenterCoordinates)
Diameter_Array = np.asarray(Diameter)
dist_check = ((CenterCoord_Array[:,0] - x_Center) ** 2 + (CenterCoord_Array[:,1] - y_Center) ** 2) ** 0.5
radius_check = (Diameter_Array / 2) + radius
radius_check_update = np.reshape(radius_check,(len(radius_check),1))
radius_overlap = (radius_check_update >= dist_check)
# Now actually check the overalp condition.
if np.sum([radius_overlap]) == 0:
# The new circle does not overlap so proceed.
newCircle_Found = 1
debug_value = 2
elif np.sum([radius_overlap]) == 1:
# The new circle overlaps with one other circle
overlap = np.arange(0,len(radius_overlap), dtype=int)
overlap_update = np.reshape(overlap,(len(overlap),1))
overlap_logix = (radius_overlap == 1)
idx_true = overlap_update[overlap_logix]
radius = dist_check(idx_true,1) - (Diameter(idx_true,1) / 2)
A similar result for the same run was produced for variables:
radius_check_update
radius_overlap
overlap_update
Here is the same code snippet for the working MATLAB version (as requested):
distcheck = ((Circles.CenterCoordinates(1,:)-x_Center).^2 + (Circles.CenterCoordinates(2,:)-y_Center).^2).^0.5;
radius_check = (Circles.Diameter ./ 2) + radius;
radius_overlap = (radius_check >= distcheck);
% Now actually check the overalp condition.
if sum(radius_overlap) == 0
% The new circle does not overlap so proceed.
newCircle_Found = 1;
debug_value = 2;
elseif sum(radius_overlap) == 1
% The new circle overlaps with one other circle
temp = 1:size(radius_overlap,2);
idx_true = temp(radius_overlap == 1);
radius = distcheck(1,idx_true) - (Circles.Diameter(1,idx_true)/2);
In the Python version I have created arrays from lists to more easily operate on the contents (the first two lines of the code snippet). The array within array result and creating arrays to access data suggests to me that I have incorrectly declared variable types, but I am not sure. Furthermore, some variables have a size, for example, (2L,) (the numerical dimension will change as circles are placed) where there is no second dimension. This produces obvious problems when I try to use the array in an operation with another array with a size (2L,1L). Because of these problems I started reshaping arrays, and then I stopped because I decided these were hacks because I had declared one, or more than one variable incorrectly. Second, for the same run I encountered the following error:
TypeError: 'numpy.ndarray' object is not callable
for the operation:
radius = dist_check(idx_true,1) - (Diameter(idx_true,1) / 2)
which occurs at the bottom of the above code snippet. I have posted the entire script at the following link because it is probably more useful to execute the script for oneself:
https://github.com/smchartrand/MarkovProcess_Bedload
I have set-up the code to run with some initial parameter values so decisions do not need to be made; these parameter values produce the expected results in the MATLAB-based script, which look something like this when plotted:
So, I seem to specifically be having issues with operations on lines 151-165, depending on the test value np.sum([radius_overlap]) and I think it is because I incorrectly declared variable types, but I am really not sure. I can say with confidence that the Python version and the MATLAB version are consistent in output through the first step of the while loop, and code line 127 which is entering the second step of the while loop. Below this point in the code the above documented issues eventually cause the script to crash. Sometimes the script executes to 15% complete, and sometimes it does not make it to 5% - this is due to the random nature of circle placement. I am preparing the code in the Spyder (Python 2.7) IDE and will share the working code publicly as a part of my research. I would greatly appreciate any help that can be offered to identify my mistakes and misapplications of python coding practice.
I believe I have answered my own question, and maybe it will be of use for someone down the road. The main sources of instruction for me can be found at the following three web pages:
Stackoverflow Question 176011
SciPy FAQ
SciPy NumPy for Matlab users
The third web page was very helpful for me coming from MATLAB. Here is the modified and working python code snippet which relates to the original snippet provided above:
dist_check = ((CenterCoordinates[0,:] - x_Center) ** 2 + (CenterCoordinates[1,:] - y_Center) ** 2) ** 0.5
radius_check = (Diameter / 2) + radius
radius_overlap = (radius_check >= dist_check)
# Now actually check the overalp condition.
if np.sum([radius_overlap]) == 0:
# The new circle does not overlap so proceed.
newCircle_Found = 1
debug_value = 2
elif np.sum([radius_overlap]) == 1:
# The new circle overlaps with one other circle
overlap = np.arange(0,len(radius_overlap[0]), dtype=int).reshape(1, len(radius_overlap[0]))
overlap_logix = (radius_overlap == 1)
idx_true = overlap[overlap_logix]
radius = dist_check[idx_true] - (Diameter[0,idx_true] / 2)
In the end it was clear to me that it was more straightforward for this example to use numpy arrays vs. lists to store results for each iteration of filling the rectangular area. For the corrected code snippet this means I initialized the variables:
CenterCoordinates, and
Diameter
as numpy arrays whereas I initialized them as lists in the posted question. This made a few mathematical operations more straightforward. I was also incorrectly indexing into variables with parentheses () as opposed to the correct method using brackets []. Here is an example of a correction I made which helped the code execute as envisioned:
Incorrect: radius = dist_check(idx_true,1) - (Diameter(idx_true,1) / 2)
Correct: radius = dist_check[idx_true] - (Diameter[0,idx_true] / 2)
This example also shows that I had issues with array dimensions which I corrected variable by variable. I am still not sure if my working code is the most pythonic or most efficient way to fill a rectangular area in a random fashion, but I have tested it about 100 times with success. The revised and working code can be downloaded here:
Working Python Script to Randomly Fill Rectangular Area with Circles
Here is an image of a final results for a successful run of the working code:
The main lessons for me were (1) numpy arrays are more efficient for repetitive numerical calculations, and (2) dimensionality of arrays which I created were not always what I expected them to be and care must be practiced when establishing arrays. Thanks to those who looked at my question and asked for clarification.
I have a simple FMU file which contains a sine block that takes u as input and outputs y. In this case, u is set to equal to time. In my C++ code I have loaded the FMI library from FMILibrary and had done all the necessary steps up to a point where I want to give my input u a new value of pi(as 3.14). So I went:
fmistatus = fmi2_import_set_real(fmu, &uRef, 1, &pi);
while (timeCurrent < timeEnd){
fmistatus = fmi2_import_do_step(fmu, timeCurrent , stepSize, fmi2_true);
timeCurrent += stepSize;
}
u was still set to time even though I tried to give it a new value. Did I miss something?
PS. Is there anywhere I can find a more detailed description on the FMI library functions? Currently I can only find input output descriptions or did I miss something again.
UPDATE: After a few trials, I think this issue might be because I was trying to redefine my equation u = time. In other words when I change my u variable into RealInput block in openmodelica everything goes fine. So what if I really wants to redefine a certain equation? what do I have to do?
You shall not be able to set any variable in FMI - and especially not a variable with a binding equation - and I assume your Modelica model has "u=time;". Instead of having "u=time" you need to add a top-level input without any equation (so that the exported FMI has it as an input) - and then connect that to the sine-block.
Details:
For a co-simulation FMI the restriction on what you can set are in the state-diagram in section 4.2.4 of FMI2 specification.
Between fmi2DoStep you can only set Real variables that have causality="input" or causality="parameter" and variability="tunable" - and an input with an equation doesn't qualify.
Before starting the integration you could set it for other variables as well, but that is only guess-values for the initialization - and should not over-write the "u=time" equation.
I was trying to write my own K-Means clustering algorithm however it is not working.Can someone take a look and help me finding what mistake I am committing.I am fairly new.
I expect the data to be clustered in 2 groups since K=2.However I am not getting the expected result.I think mean assignment is not working properly.Can someone give a look?
https://github.com/DivJ/Robo_Lab/blob/master/K_Means.py
dist=[]
lab=[]
x_sum,y_sum=0,0
x_sum1,y_sum1=0,0
k=2
mean=pt[:k]
def assignment():
global dist
global lab
for i in range(0,100):
for j in range(0,k):
dist.append(math.hypot(pt[i,0]-mean[j,0],pt[i,1]-mean[j,1]))
lab.append(dist.index(min(dist)))
dist=[]
def mean_shift():
global x_sum,x_sum1,y_sum,y_sum1,lab
for i in range(0,100):
if(lab[i]==0):
plt.scatter(pt[i,0],pt[i,1],c='r')
x_sum=pt[i,0]+x_sum
y_sum=pt[i,1]+y_sum
elif(lab[i]==1):
plt.scatter(pt[i,0],pt[i,1],c='b')
x_sum1=pt[i,0]+x_sum1
y_sum1=pt[i,1]+y_sum1
mean[0,0]=x_sum/lab.count(0)
mean[0,1]=y_sum/lab.count(0)
mean[1,0]=x_sum1/lab.count(1)
mean[1,1]=y_sum1/lab.count(1)
lab=[]
def k_means(itr):
for z in range(0,itr):
assignment()
mean_shift()
k_means(100)
Here's what's wrong with your code:
1) You initialize means as pt[:k], however later you reassign means which leads to the first two points being reassigned unintentionally since means merely is a pointer to these points. You need to create a copy of the first to points to avoid changing them:
import copy
means=copy.copy(pt[:k])
2) You initialize x_sum, y_sum, x_sum1 and y_sum1 outside of mean_shift() which causes the sums to grow bigger and bigger with each iteration. Set them to 0 every time you call mean_shift().
I am using the IBM cplex optimizer to solve an optimization problem and I don't want all terminal prints that the optimizer does. Is there a member that turns this off in the IloCplex or IloModel class? These are the prints about cuts and iterations. Prints to the terminal are expensive and my problem will eventually be on the order of millions of variables and I don't want to waste time with these superfluous outputs. Thanks.
Using cplex/concert, you can completely turn off cplex's logging to the console with
cpx.setOut(env.getNullStream())
Where cpx is an IloCplex object. You can also use the setOut function to redirect logs to a file.
There are several cplex parameters to control what gets logged, for example MIPInterval will set the number of MIP nodes searched between lines. Turning MIPDisplay to 0 will turn off the display of cuts except when new solutions are found, while MIPDisplay of 5 will show detailed information about every lp-subproblem.
Logging-related parameters include MIPInterval MIPDisplay SimDisplay BarDisplay NetDisplay
You set parameters with the setParam function.
cpx.setParam(IloCplex::MIPInterval, 1000)