by Peter Standaart & Mj Patterson of the 'Gators' Research Group
We (Peter Standaart and Mj Patterson) are involved with a group called the AstroInvestigators or The Gators for short. The Gators are led by engineer and astrologer Alphee Lavoie. Alphee brings his engineering skills and acumen to a field which historically had been dismissed as not worthy of study by the larger scientific community. That is why Alphee has developed research software employing statistical analysis and neural network capability, so that serious inquiry may now be made.
The birthchart or horoscope is simply a fisheye photograph of the planets and luminaries (Sun and Moon) around the Earth at the precise moment of a person’s birth. Whereas 4 minutes’ difference causes the chart to vary significantly, the accurate time of birth is necessary for a clean data analysis. Happily, Lois Rodden has gifted researchers with a huge collection of carefully verified data which now resides at astro.com and is known as the AstroDatabank. We only used AA data for this research, meaning exact recorded time.
PURPOSE OF STUDY
The purpose of our study is to develop a neural network model which is a collection of astrological significators appearing in the birth charts of people who become wealthy through their own efforts. If we are successful we should not see the charts of people who inherited or married into money, rather only those who became wealthy through their own hard work or invention.
We used Alphee Lavoie’s statistical engine ‘Fast Research’ (previously known as AstroInvestigator) to study and group this data.
We selected 475 charts from AstroDatabank all of AA quality all tagged with #financial gain through career.
Control Group Data:
We decided that we wanted a control group which had some chance of not containing charts of wealthy people so we went to the AstroDatabank and selected only charts of people in careers unlikely to bring much income, such as office workers, landscapers, waiters, kitchen help and similar. In total we pulled 477 AA charts and this is what we are using to control results.
Fast Research Settings:
Criteria – Chi Sq 3
Chi Sq 3
Occurrence 17 (roughly 5% of Test File)
Test File: Gain thru success - all.mil (473 AA charts from ADB)
Control File: Regular workers.mil (477 AA charts from ADB)
Model: Standard Only
We set aside 25% of each of these files for use in the Neural Net
If you haven't played with Chi Square before, here is a video introducing the concept.
We took a careful look at the initial results to inspect for generational bias. For instance, we noticed that there were 39 instances of Saturn in Aquarius and we needed to see whether that was due to that being an indicator or more to do with the selection of birth years in the data we input for the model. So we looked through the raw data to verify whether we had inadvertently skewed our data by including a preponderance of charts with this value.
The results were encouraging: 44 of 475, or 39 of 355 (after the neural net test group of 25% was randomly removed) were born with Saturn in Aquarius, 10.9% - not statistically significant. So we were reassured that the results bringing Saturn in Aquarius into focus were to do with the neural net structure and not to do with the introduction of skewed data as input.
We got a set of results through the “calculate” function, (this uses the Chi Square statistical analysis to pull results more likely to predict our model) then saved these. We then ran “create neural net” and “learn neural net” and the results were really quite good!
We decided to see what would happen if we improved the confidence (reliability) of the model. We reran the model at 90% and it completed almost immediately! We achieved a neural net model showing good separation at 90% confidence.
NEURAL NETWORK ANALYSIS
The definition of a Neural Network from Wikipedia reads: “In machine learning, artificial neural networks (ANNs) are a family of statistical learning algorithms inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected "neurons" which can compute values from inputs, and are capable of machine learning as well as pattern recognition thanks to their adaptive nature.”
It is also called a Black Box model, as Franco Soulbody explains in his similar research into breast cancer, because what happens between the input and output is not known. The Fast Research programme has the capability to take all the contributors from the model based on what occurs often and what occurs seldom and develop a Neural Network which can predict the outcome based on all aspects of the chart.
Here is a YouTube video offering a basic explanation of neural networking https://www.youtube.com/watch?v=DG5-UyRBQD4
The figures below show how the model behaves for those who made their own wealth and those who did not get wealthy. As you can see, this neural net is a good predictor of wealth and of those who did not become wealthy.
Here is an example of the Neural net ‘black box’ learning from the data what would constitute a strong YES what would be a strong NO and what would be an unclear result known in the program as DON’T KNOW. This ran to 90% certainty – an exciting result!
In each of these diagrams, Red is an indicator of YES, Blue of NO and Purple of DON’T KNOW.
Some interesting results arose also when we ran the charts of ‘ordinary workers’ through the same neural net: we got 2 ‘outliers’ shown here below – Alex Haley and Paul von Hindenburg. So we looked up the bios of these two and discovered that they were in fact in the wrong category in that they were both very wealthy by their own efforts.
So delightfully, this supports our neural net as they flagged up beautifully for success under that model.
One of the charts in the ‘success’ file was surprisingly low, so we investigated this chart ( Otis Chandler ) to discover that he did not gain his wealth through his own efforts, rather he inherited it from a 6th generation publishing dynasty. This was a delightful verification that the model is specific in its selection of wealth BY OWN EFFORT which is what we had originally postulated.
The purpose of our study was to develop a neural network model which is a collection of astrological significators appearing in the birth charts of people who become wealthy through their own efforts.
This neural net model at which we arrived clearly demonstrates a good predictor for success through one’s own hard work, and not through inheritance, marriage or windfall, to a confidence level of 90%.
In terms of our methodology, we discovered that using standard astrological events as input to the neural network was best. That is to say allowing the neural network to ‘learn’ its own set of aspects and planetary relationships was by far the most accurate methodology and we will be using it going forward.
For a list of the events selected please refer to Appendix A below.
Appendix A – Neural Net Weighted Events