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cottoneyedjoe

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March 28, 2019
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Sep 22, 2024, 4:37 pm

Dr. San maybe you should start a podcast. I think there might be a good audience for your brand of humor and lottery nuttery in Brazil.
dr san

bgonÃ§alves
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Sep 22, 2024, 5:19 pm

hello COTTO=Randomness and Biology: Genetics, for example, involves a random component in the combination of genes from parents, resulting in unique characteristics in each individual. Did you know that when you assign men (even) to women (odd) in small, medium and large cities, there is no imbalance in the inhabitants, of having more men or women, there is balance. What is behind this? The probability of being born male or female is 50% in lotteries. Each pair of even x odd numbers from the last draw or another reference, can be odd or even?
AllenB

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Sep 22, 2024, 8:43 pm

Quote: Originally posted by cottoneyedjoe on Sep 22, 2024
Dr. San maybe you should start a podcast. I think there might be a good audience for your brand of humor and lottery nuttery in Brazil.

Maybe this Topic is abour Scoring, If you know what I mean.
dr san

bgonÃ§alves
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Sep 22, 2024, 9:24 pm

corret allenb =the relationship is about getting things right, using delays and repetitions, choosing and changing which ones will repeat and the delays play until reaching the climax (in this case, hitting the grand prize)
Stat$talker

700 light yrs West of Milky Way Galaxy's Center
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Sep 22, 2024, 11:02 pm

Quote: Originally posted by AllenB on Sep 22, 2024
Maybe this Topic is abour Scoring, If you know what I mean.

He just maay be tawkin bout thoze who are

Digitally Empotent.

Cain't git that Numba up !
hypersoniq

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Sep 23, 2024, 7:57 am

Apophenia, that is the tendency to look for what may appear to be meaningful patterns in random data.

When generalized to stimuli, it is Pareidolia, the basis for the Rorschach ink blot test, or the simple childhood game of trying to find recognizable shapes in cloud formations.

The delays and repeats observed might fall into this category, the same as my follower distribution data does.

I do not see patterns in randomness, regardless of how many times I have tried. I just might be the worst person in the world to have this hobby. At this point I remain half heartedly in the game probably because it is an ego thing... I must solve the puzzle! But the reasonable expectation is that I will not. I do, however come up with some creative attempts at times.

I think this may require a better understanding of patterns in general, and of the time series analysis (using categorization rather than moving averages) of univariate data that forms uniform distributions, as lottery histories tend to do.

There are unsupervised machine learning algorithms that excel in this area. It might be worth investigating these. Let the code search for patterns instead, and report on their statistical significance. It could be that such algorithms might see what you are saying about delays and repeats...

If the patterns found could be used in a supervised learning model, Machine learning could end up doing what excel sheets and graphs could not.

If a jackpot win is the big O, machine learning could be the "sex robot" that makes it happen... 😎

Have an EXCELlent day!
dr san

bgonÃ§alves
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Sep 23, 2024, 9:52 am

Hello, correct hyper, the sex was just to show the exercise movement, back and forth, repetitions and delays, you can create a filter of the endings from 0 to 9 of a lottery e.g. 5/39, you can track the first digit separated e.g. = 18 analyze the 1 separated from the ending or last digit 8 then assemble the number, the lotteries are summarized in having a reference e.g. the last draw has repetitions and delays both in the number or the two digits of the number separated, the endings filter is a great filter, hyper, assemble three groups of delays, short, medium and long and three groups of repetitions by frequency
Stat$talker

700 light yrs West of Milky Way Galaxy's Center
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September 1, 2019
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Sep 23, 2024, 10:06 am

Quote: Originally posted by hypersoniq on Sep 23, 2024
Apophenia, that is the tendency to look for what may appear to be meaningful patterns in random data.

When generalized to stimuli, it is Pareidolia, the basis for the Rorschach ink blot test, or the simple childhood game of trying to find recognizable shapes in cloud formations.

The delays and repeats observed might fall into this category, the same as my follower distribution data does.

I do not see patterns in randomness, regardless of how many times I have tried. I just might be the worst person in the world to have this hobby. At this point I remain half heartedly in the game probably because it is an ego thing... I must solve the puzzle! But the reasonable expectation is that I will not. I do, however come up with some creative attempts at times.

I think this may require a better understanding of patterns in general, and of the time series analysis (using categorization rather than moving averages) of univariate data that forms uniform distributions, as lottery histories tend to do.

There are unsupervised machine learning algorithms that excel in this area. It might be worth investigating these. Let the code search for patterns instead, and report on their statistical significance. It could be that such algorithms might see what you are saying about delays and repeats...

If the patterns found could be used in a supervised learning model, Machine learning could end up doing what excel sheets and graphs could not.

If a jackpot win is the big O, machine learning could be the "sex robot" that makes it happen... 😎

"I remain half heartedly in the game probably because"

XZACTLY..!

The Only reazon whyy I remain a Staunch allie of Probability'z TRUE PrEdIcTiOnZ...

But , sometimez?

I DO SMELL A DUERTY RAT

-Stat$talker
dr san

bgonÃ§alves
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Sep 23, 2024, 4:37 pm

hello=

How to apply an RNN to predict lottery results:

Data Preparation:

Collection: Gather a historical dataset with the results of all draws of the lottery in question.

Preprocessing: Transform the data into a format suitable for the RNN. This may involve normalization, creation of sliding windows (to define sequences of past numbers as input) and one-hot encoding (to represent each number as a binary vector).

Building the Neural Network:

Architecture: Choose a type of RNN, such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit), which are more efficient variants and capable of dealing with the vanishing gradient problem.

Layers: Define the number of hidden layers and the number of neurons in each layer.

Activation Function: Choose an appropriate activation function for the layers of the network.

Loss Function: Define a loss function that will measure the difference between the network's predictions and the actual results. The most common loss function for prediction problems is the mean squared error (MSE).

Network Training:

Data Splitting: Split the data into training, validation, and test sets.

Optimizer: Choose an optimizer, such as Adam or RMSprop, to adjust the network weights during training.

Epochs: Set the number of times the network will be trained on the entire dataset.

Batch Size: Set the size of the batch of data that will be used for each weight update.

Prediction:

Input: Provide the trained network with the most recent sequence of outcomes as input.

Output: The network will generate a prediction for the next draw.

Challenges and Considerations:

Randomness: Even with a well-trained RNN, lottery prediction will always be uncertain due to the random nature of the process.

Overfitting: It is critical to avoid overfitting, as the network may memorize the training data instead of generalizing to new data.
Interpretability: RNNs are black-box models, which makes it difficult to interpret the results.
Computational Cost: Training an RNN can be computationally expensive, especially for large data sets.
Other Approaches to Explore:

Distribution Mixture Models: Model the probability distribution of the drawn numbers as a mixture of simpler distributions.
Genetic Algorithms: Use genetic algorithms to generate and evolve populations of possible number combinations.
Game Theory: Model the lottery as a game between players and analyze the optimal strategies.
Stat$talker

700 light yrs West of Milky Way Galaxy's Center
United States
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September 1, 2019
5,504 Posts
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Sep 23, 2024, 5:53 pm

Quote: Originally posted by dr san on Sep 23, 2024
hello=

How to apply an RNN to predict lottery results:

Data Preparation:

Collection: Gather a historical dataset with the results of all draws of the lottery in question.

Preprocessing: Transform the data into a format suitable for the RNN. This may involve normalization, creation of sliding windows (to define sequences of past numbers as input) and one-hot encoding (to represent each number as a binary vector).

Building the Neural Network:

Architecture: Choose a type of RNN, such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit), which are more efficient variants and capable of dealing with the vanishing gradient problem.

Layers: Define the number of hidden layers and the number of neurons in each layer.

Activation Function: Choose an appropriate activation function for the layers of the network.

Loss Function: Define a loss function that will measure the difference between the network's predictions and the actual results. The most common loss function for prediction problems is the mean squared error (MSE).

Network Training:

Data Splitting: Split the data into training, validation, and test sets.

Optimizer: Choose an optimizer, such as Adam or RMSprop, to adjust the network weights during training.

Epochs: Set the number of times the network will be trained on the entire dataset.

Batch Size: Set the size of the batch of data that will be used for each weight update.

Prediction:

Input: Provide the trained network with the most recent sequence of outcomes as input.

Output: The network will generate a prediction for the next draw.

Challenges and Considerations:

Randomness: Even with a well-trained RNN, lottery prediction will always be uncertain due to the random nature of the process.

Overfitting: It is critical to avoid overfitting, as the network may memorize the training data instead of generalizing to new data.
Interpretability: RNNs are black-box models, which makes it difficult to interpret the results.
Computational Cost: Training an RNN can be computationally expensive, especially for large data sets.
Other Approaches to Explore:

Distribution Mixture Models: Model the probability distribution of the drawn numbers as a mixture of simpler distributions.
Genetic Algorithms: Use genetic algorithms to generate and evolve populations of possible number combinations.
Game Theory: Model the lottery as a game between players and analyze the optimal strategies.

Ookaay, naah?

Do WE git the LOAN...?
Cmoore50

United States
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December 3, 2018
621 Posts
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Sep 23, 2024, 5:54 pm

Quote: Originally posted by dr san on Sep 23, 2024
hello=

How to apply an RNN to predict lottery results:

Data Preparation:

Collection: Gather a historical dataset with the results of all draws of the lottery in question.

Preprocessing: Transform the data into a format suitable for the RNN. This may involve normalization, creation of sliding windows (to define sequences of past numbers as input) and one-hot encoding (to represent each number as a binary vector).

Building the Neural Network:

Architecture: Choose a type of RNN, such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit), which are more efficient variants and capable of dealing with the vanishing gradient problem.

Layers: Define the number of hidden layers and the number of neurons in each layer.

Activation Function: Choose an appropriate activation function for the layers of the network.

Loss Function: Define a loss function that will measure the difference between the network's predictions and the actual results. The most common loss function for prediction problems is the mean squared error (MSE).

Network Training:

Data Splitting: Split the data into training, validation, and test sets.

Optimizer: Choose an optimizer, such as Adam or RMSprop, to adjust the network weights during training.

Epochs: Set the number of times the network will be trained on the entire dataset.

Batch Size: Set the size of the batch of data that will be used for each weight update.

Prediction:

Input: Provide the trained network with the most recent sequence of outcomes as input.

Output: The network will generate a prediction for the next draw.

Challenges and Considerations:

Randomness: Even with a well-trained RNN, lottery prediction will always be uncertain due to the random nature of the process.

Overfitting: It is critical to avoid overfitting, as the network may memorize the training data instead of generalizing to new data.
Interpretability: RNNs are black-box models, which makes it difficult to interpret the results.
Computational Cost: Training an RNN can be computationally expensive, especially for large data sets.
Other Approaches to Explore:

Distribution Mixture Models: Model the probability distribution of the drawn numbers as a mixture of simpler distributions.
Genetic Algorithms: Use genetic algorithms to generate and evolve populations of possible number combinations.
Game Theory: Model the lottery as a game between players and analyze the optimal strategies.

Let us know when you start on this.

GET IT DONE
MRLOTTO69

Ontario
Canada
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August 29, 2024
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Sep 23, 2024, 7:24 pm

Hope is for the hopeless . Our chance of winning the jackpot is the same as not playing the lottery...lol.

Edgar Cayce - "Nothing happens by chance, nothing is as good luck ,as commonly called, for the cause must be present to produce that which is real, see ?"

The cause of you winning the jackpot is by having your selected numbers matching the drawn numbers.

For example to win the 6/49 lottery draw, your selected 6 number ticket must match one of the 13,983,816 six number combinations drawn.

That makes your odds 1 in 13,983,816 to win.

The logical reason why some people win the jackpot is because their selected numbers matched the numbers drawn. That is not luck.

I found that the harder I work, the luckier I get...lol.
dr san

bgonÃ§alves
Brasil
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June 9, 2010
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Sep 23, 2024, 9:49 pm

ok, in lotteries you have to know how to look for references!! references in numbers, patterns, etc. in 80% of the time are enough
Stack47

Kentucky
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February 14, 2006
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Sep 25, 2024, 1:13 am

Quote: Originally posted by dr san on Sep 23, 2024
ok, in lotteries you have to know how to look for references!! references in numbers, patterns, etc. in 80% of the time are enough

Ever consider trying something very simple for a pick 3 or 4 game like taking 10 cards, ace to 10 out of a deck of playing cards, shuffle them, draw a card, write down the number and repeat until all 10 cards are exposed? Play the first three numbers, the second, third and fourth, third, fourth and fifth, etc and get a guaranteed 1 in 100 chance of winning.

Or how about keying on any 3 numbers or any 3 numbers of your choosing in a 5/39 game? Saw a guy pick 3 numbers, paired the other 36 numbers on 18 lines and won a $100,000 plus jackpot.

It's an old saying "Keep it simple s****d!"

"Some people have something to say while other people feel the need to say something"