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Home Environmental Monitoring System design (PC Upper machine Edition) (mobile app version to be determined)

Here is my graduation design: Home Environmental Monitoring System design (PC on the temporary version of the computer. Mobile app version is pending). This system adopts STC12C5A60S2 single chip microcomputer. Combined with the sensor. The air humidity, air temperature, pressure, altitude, inlet temperature, water temperature, light intensity, soil moisture, combustible gas concentration, personnel in and

Machine learning system Design---Error metrics for skewed (skewed) classes

(actually is negative, forecast is also negative), false negative (forecast also negative, actually is positive).Precision (Precision): true positive (true positive)/predicted number of positive (positive) (predicted y=1) = True positive/(true positive + false positive)Recall (Recall): true positive (true positive)/actual number of positive (positive) (actually has cancer) = True positive/(true positive + false negative)So when we use precision and recall to evaluate the algorithm of the previo

Machine Learning System Design Study Notes (2)

minimum value (that is, the best fit to the data) fp1, residuals, rank, sv, rcond = sp.polyfit(x,y,1,full =True) print fp1 FP1 is a two-dimensional array with values of A and B. The printed value is [2.59619213, 989.02487106]. We obtain the linear function f (x) = 2.59619213x + 989.02487106. What is its error? Do you still remember the error function? We construct a function using the following code: f1 = sp.poly1d(fp1)print (error(f1,x,y)) We get a result: 317389767.34 is the result? Not

Machine learning System Design----Learning system

The process of building a machine learning algorithm: Quickly build a simple algorithm and test the performance of the algorithm with a cross-validation set. Draw the learning curve, check whether the algorithm has high variance or high deviation problem, so as to choose corresponding coping methods. Error analysis, to see the examples of errors in the algorithm, to analyze whether these instances have some systematic trend. Eval

Paper:synthesizable finit state Machine design techniques using the new SystemVerilog 3.0 Enhancements Enhanced Coding sty Les

The code for the 1.ANSI style is relatively compact.the following specifications recommend, better.The following is a complete specification of the module header with parameterGeneral 1bit, everyone is wire signal1 = gen_signal1_logic; This notation. Does not seem to be directly assign Signal1=gen_signal1_logic, does not declare wire signals;.Individuals generally use [email protected] (*), feel with a () so 1. Easy to follow the unity of SEQ 2. Easy to see *.Paper:synthesizable finit state

Stanford University public Class machine learning: Machines Learning System Design | Trading off precision and recall (F score formula: How to balance (trade-off) precision and recall values in a learning algorithm)

take an average of this evaluation mode.It is a useful algorithm to use the F-score algorithm to evaluate both precision and recall rates . The PR of the molecule determines that the precision ratio (P) and recall (R) must be large at the same time to ensure that the F score values are larger. If the precision ratio or recall rate is very low, close to 0, the direct result of the PR value is very low, approaching 0, that is, F score is also very low.At this point we compare three algorithms, we

Java language Program design on-machine Experiment 4 Master Declaration interface, a class implementation interface declaration and use method

"; a[7]= "H"; a[8]= "I"; a[9]= "J"; a[10]= "K"; a[11]= "L"; a[12]= "M"; a[13]= "n"; a[14]= "O"; for(inti=0;i){ for(intj=i+1;j){ if(A[i].compareto (a[j]) >0) {C=A[i]; A[i]=A[j]; A[J]=C; } } } if(Sxcz (b,a)) {System.out.println ("The sequential lookup method has been found"); } Else{System.out.println ("Order Lookup not Found"); } if(Efcz (b,a)) {System.out.println ("Binary search method has been found"); } Else{System.out.p

Paper:synthesizable finite state machine design techniques using the new SystemVerilog 3.0 enhancements's standard Verilog FSM conding Styles (two-segment)

1.TWO always block style with combinational outputs (good style)The corresponding code is as follows:2-Paragraph summary:(1) The combinational always block sensitivity list was sensitve to changes on the state variable and all of the inputs Refe Renced in the combinaltional always block.This is generally used [email protected] (*) on the OK.(2) The combinaltional always block have a default next state assignments at the top of the "Always Block"(3) Default output assignments is made prior to the

Scala program Design-java virtual machine multi-core programming practice (i)

Scala, inheriting a base class is similar to Java, with a two-point limit, and (1) Overriding the method requires the Override keyword, (2) only the primary constructor can pass parameters to the base class constructor. Markerfactory is a single case, once the definition is complete, its name represents the only instance of the singleton object, and the singleton can be passed to the function as if it were a typical passing instance. class Marker (Val color:string) {prin

Atitit. Virtual machine-based initiator design--java launcher java generation EXE

Atitit. design of launcher based on virtual machine --java Launcher java generation exeExe4j vs Nativej1. Step1 Read configuration file 11.1. Regular mode , allinone exe mode 11.2. Gene EXE DIR CFG 32. JRE directory configuration and search 33.-classpath-djava.ext.dirs jar and native DLL configuration 54. Main class configuration 65. The other 65.1. Configuring version Information 65.2. GUI with console s

Stanford University public Class machine learning: Machines Learning System Design | Error metrics for skewed classes (definition of skew class issues and evaluation measures for skew class issues: precision ratio (precision) and recall rate (recall))

classification model, which gives us a better evaluation value and gives us a more direct way to evaluate the good and bad of the model. One last thing to keep in mind, in the definition of precision and recall, we define precision and recall rates, and we habitually use Y=1 to show that this class appears very little. So if we try to detect a very rare situation, like cancer. I hope it's a rare situation where precision and recall are defined as Y=1 rather than y=0, as some of the fewer classe

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