I want to draw activity diagram to test a specific phone. I have tried to start with synchronous operation on choosing 3 random phone . can you please check if I have done them correctly , just started learning this process and couldn't figure out if I have to use synchronous or loop for choosing 3 random phone as well
To that I am choosing 3 same type phone randomly for the test.
Initially I check for packaging damage - if it had damage then return to production manager
If no package damage - then open box and check for physical damage - if damage found then return to production manger
if no physical damage then test for functionality damage - if no damage then consider the product as Test passed.
The first horizontal bar is a fork node, meaning that the control flow is split into three concurrent threads. So A1, A2, and A3 are three actions that are executed in parallel. The second horizontal bar is a join node, meaning that once that all of these actions have been finished, the control flow will continue (with the first decision).
I think that this is not what you want to show: The first action "Randomly take 3 samples" already gives you three samples and you would process each one of them in the same way, so you should rather have a loop with three iterations around the part with the decisions.
In general, try distinguish between actions, objects and states - A1, A2, A3 seem to be objects and not actions, and "Test passed" is the resulting state and not an action either.
The part with the three decisions itself is fine (except that personally I like to draw the control flow either from top to bottom or from left to right, but not back - but this is a matter of taste).
Here is an introduction to activity diagrams with some good examples.
Related
I am using opencv and openvino and am trying to figure out when I have a face detected, use the cv2.rectangle and have my coordinates sent but only on the first person bounded by the box so it can move the motors because when it sees multiple people it sends multiple coordinates and thus causing the servo and stepper motors to go crazy. Any help would be appreciated. Thank you
Generally, each code would run line by line. You'll need to create a proper function for each scenario so that the data could be handled and processed properly. In short, you'll need to implement error handling and data handling (probably more than these, depending on your software/hardware design). If you are trying to implement multiple threads of executions at the same time, it is better to use multithreading.
Besides, you are using 2 types of motors. Simply taking in all data is inefficient and prone to cause missing data. You'll need to be clear about what servo motor and stepper motor tasks are, the relations between coordinates, who will trigger what, if something fails or some sequence is missing then do task X, etc.
For example, the sequence of Data A should produce Result A but it is halted halfway because Data B went into the buffer and interfered with Result A and at the same time screwed Result B which was anticipated to happen. (This is what happened in your program)
It's good to review and design your whole process by creating a coding flowchart (a diagram that represents an algorithm). It will give you a clear idea of what should happen for each sequence of code. Then, design a proper handler for each situation.
Can you share more insights of your (pseudo-)code, please?
It sounds easy - you trigger a face-detection inference-request and you get a list/vector with all detected faces (the region-of-interest for each detected face) (including false-positive and false-positives, requiring some consistency-checks to filter those).
If you are interested in the first detected face only - then it could be to just process the first returned result from the list/vector.
However, you will see that sometimes the order of results might change, i.e. when 2 faces A and B were detected, in the next run it could still return faces, but B first and then A.
You could add object-tracking on top of face-detection to make sure you always process the same face.
(But even that could fail sometimes)
I have a pipeline in Beam that uses CoGroupByKey to combine 2 PCollections, first one reads from a Pub/Sub subscription and the second one uses the same PCollection, but enriches the data by looking up additional information from a table, using JdbcIO.readAll. So there is no way there would be data in the second PCollection without it being there in the first one.
There is a fixed window of 10seconds with an event based trigger like below;
Repeatedly.forever(
AfterWatermark.pastEndOfWindow().withEarlyFirings(
AfterProcessingTime.pastFirstElementInPane().plusDelayOf(Duration.standardSeconds(40))
).withLateFirings(AfterPane.elementCountAtLeast(1))
);
The issue I am seeing is that when I stop the pipeline using the Drain mode, it seems to be randomly generating elements for the second PCollection when there has not been any messages coming in to the input Pub/Sub topic. This also happens randomly when the pipeline is running as well, but not consistent, but when draining the pipeline I have been able to consistently reproduce this.
Please find the variation in input vs output below;
You are using a non-deterministic triggering, which means the output is sensitive to the exact ordering in which events come in. Another way to look at this is that CoGBK does not wait for both sides to come in; the trigger starts ticking as soon as either side comes in.
For example, lets call your PCollections A and A' respectively, and assume they each have two elements a1, a2, a1', and a2' (of common provenance).
Suppose a1 and a1' come into the CoGBK, 39 seconds passes, and then a2 comes in (on the same key), another 2 seconds pass, then a2' comes in. The CoGBK will output ([a1, a2], [a1']) when the 40-second mark hits, and then when the window closes ([], [a2']) will get emitted. (Even if everything is on the same key, this could happen occasionally if there is more than a 40-second walltime delay going through the longer path, and will almost certainly happen for any late data (each side will fire separately).
Draining makes things worse, e.g. I think all processing time triggers fire immediately.
Simple question on hello world example of the MCTS for tic-tac-toe,
Let's assume we are given a board and we want to make an optimal decision. As I undestand the choice of consecutive nodes while simulation (until leaf is met) is determined by a exploration/exploitation trade-off function (as described on wikipedia). I really wonder what is the intuition behind first component (exploitation) of the function here, especially for games between two players with oppposite goals. Then the meaning of "the most promising" changes depending on who makes a move. Shouldn't this function change depeding on who makes the next move (especially its first component)?
Yes, that exploitation part of the equation should be implemented to take into account the evaluations from the perspective of the agent/player who gets to select an action in that node.
For single-agent settings, the implementation is straightforward; simply always maximize.
For zero-sum, turn-based, two-player settings, you'd want to alternate between maximizing or minimizing that exploitation part of the equation (note: always maximize the exploration term!). This can also be implemented by simply multiplying that term by -1 in nodes where the opponent gets to move.
Other settings are possible too, but require slightly more implementation effort (e.g. keeping different average scores for different players in settings which are not zero-sum or have more than two players)
If a technical indicator works very slow, and I wish to include it in an EA ( using iCustom() ), is there a some "wrapper" that could cache the indicator results to a file based on the particular indicator inputs?
This way I could get a better speed next time when I backtest it using the same set of parameters, since the "wrapper" could read the result from file rather than recalculate the result from the indicator.
I heard that some developers did that for their needs in order to speed up backtesting, but as far as i know, there's no publicly available solution.
If I had to solve this problem, I would create a class with two fields (datetime and indicator value, or N buffers of the indicator), and a collection class similar to CArrayObj.mqh but with an option to apply binary search, or to start looking for element from a specific index, not from the very beginning of the array.
Recent MT4 Builds added VERY restrictive conditions for Indicators
In early years of MT4, this was not so cruel as it is these days.
FACT#1: fileIO is 10.000x ~ 100.000x slower than memIO:
This means, there is no benefit from "pre-caching" values to disk.
FACT#2: Processing Performance has HARD CEILING:
All, yes ALL, Custom Indicators, that are being used in MetaTrader4 Terminal ( be it directly in GUI, or indirectly, via Template(s) or called via iCustom() calls & in Strategy Tester via .tpl + iCustom() ) ALL THESE SHARE A SINGLE THREAD ...
FACT#3: Strategy Tester has the most demanding needs for speed:
Thus - eliminate all, indeed ALL, non-core indicators from tester.tpl template and save it as "blank", to avoid any part of such non-core processing.
Next, re-design the Custom Indicator, where possible, so as to avoid any CPU-ops & MEM-allocation(s), that are not necessary.
I remember a Custom Indicatore designs with indeed deep-convolutions, which could have been re-engineered so as to keep just a triangular sparse-matrix with necessary updates, that has increased the speed of Indicator processing more than 10.000x, so code-revision is the way.
So, rather run a separate MetaTrader4 Terminal, just for BackTesting, than having to wait for many hours just due to un-compressible nature of numerical processing under a traffic-jam congestion in the shared use of the CustomIndicator-solo-Thread that none scheduling could improve.
FACT#4: O/S can increase a process priority:
Having got to the Devil's zone, it is a common practice to spin-up the PRIO for the StrategyTester MT4, up to the "RealTime PRIO" in the O/S tools.
One may even additionally "lock" this MT4-process onto a certain CPU-core(s) and setup all other processes with adjacent CPU-core-AFFINITY, so that these two distinct groups of processes do not jump one to the other group's CPU-core(s). Hard, but if squeezing the performance to the bleeding edge, this is a must.
I am trying to understand Q-Learning,
My current algorithm operates as follows:
1. A lookup table is maintained that maps a state to information about its immediate reward and utility for each action available.
2. At each state, check to see if it is contained in the lookup table and initialise it if not (With a default utility of 0).
3. Choose an action to take with a probability of:
(*ϵ* = 0>ϵ>1 - probability of taking a random action)
1-ϵ = Choosing the state-action pair with the highest utility.
ϵ = Choosing a random move.
ϵ decreases over time.
4. Update the current state's utility based on:
Q(st, at) += a[rt+1, + d.max(Q(st+1, a)) - Q(st,at)]
I am currently playing my agent against a simple heuristic player, who always takes the move that will give it the best immediate reward.
The results - The results are very poor, even after a couple hundred games, the Q-Learning agent is losing a lot more than it is winning. Furthermore, the change in win-rate is almost non-existent, especially after reaching a couple hundred games.
Am I missing something? I have implemented a couple agents:
(Rote-Learning, TD(0), TD(Lambda), Q-Learning)
But they all seem to be yielding similar, disappointing, results.
There are on the order of 10²⁰ different states in checkers, and you need to play a whole game for every update, so it will be a very, very long time until you get meaningful action values this way. Generally, you'd want a simplified state representation, like a neural network, to solve this kind of problem using reinforcement learning.
Also, a couple of caveats:
Ideally, you should update 1 value per game, because the moves in a single game are highly correlated.
You should initialize action values to small random values to avoid large policy changes from small Q updates.