MATH & ML

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### Project Portfolio (Already Made)

1. **Ellipses**

    – Visualization

    – Recursion

    – Scaled at 8/3

    – Intersection points

    – Prime numbers

    – Ramanujan-Nigel

2. **Modulo Arithmetic** + mod2 x mod7 table fitting

3. **Wonders of Number 7 Viz**

4. **Dialer**

5. **Sales Row Unfolder and CSV Convo Tracker**

6. **Dating App Simulator**

7. **Interactive and Animated Visualizations**

8. **Viz Weight Squares**

    – Sliders and numerical input to control bar plot heights

9. **Optimization Convex Functions and Different Algorithm Varieties Table**

    – Teaching and visualization convex functions

    – Beta distribution and convex theorem

    – Different types of optimizers

    – Multi-parameter dimensions

    – Non-convex functions

    – Anchor points

    – List of all types and visualizations

    – Effects on optimization

10. **Optimization and Multi-objective Optimization**

11. **Stable Matching Algorithm Variations**

12. **Multi-armed Bandit Exhaustive**

    – Variants

    – CCCB

    – List of all equations

    – Animated bar chart

    – Experiments

13. **Hypothesis Test Automated**

    – Null hypothesis and alternate

    – Variable types

    – Normal distributions

    – Gaussian models

    – P-value

    – Degrees of freedom

    – Data types and trees

    – Formulating the question

    – Table

14. **Entire Activation Function**

    – Teaching about activation functions

    – Linear regression example

    – Very advanced idea

15. **Weight Normalization and HWE and Norm vs Std vs Softmax**

16. **Godel and Quins**

    – Self-reference and Python that takes code as a variable

    – Godel encoding and concepts of prime numbers

    – Scripts as Quins

    – Homomorphic

17. **Godel Incompleteness Theorem, Russell Branch and Set Theory Paradox Explanation**

18. **Set Theory, Semiotics, and Logic Walkthrough and Knowledge Graph**

19.  **Unified Noumena Primitive Generators Lattice 7X7 of POS & NER –> Logical Syllogisms**

### Conceptualized (Not Yet Made)

1. **Thanksgiving Tree**: Automated neural tuning from activation functions as node splits and stable matching algorithm

2. **Math Equations Database Widget Equation Querier**

3. **LaTeX Control and ChatGPT Response Formatting Control**

4. **Excel Sheet and Database Maker**:

    – Widget handles and data type options

    – Automated hypothesis tester

    – Exports as Excel, CSV, PDF, pretty tables, LaTeX, Markdown, and Word

5. **RAG with Vectara API**

6. **ChatGPT Best Prompts**

7. **Neural Network Maker with Node Icons and Widget Control**

8. **AI to Create Manim Visualizations**

Code Execution Output

Python Code Example


import pandas as pd

# Initial data
data = {
    "Recursion": [1],
    "Number of Ellipses": [3],
    "Number of Chords": [6],
    "Number of Foci": [6],
    "Number of Points P": [3],
    "Ellipses": [['E_1_a', 'E_1_b', 'E_1_c']],
    "Chords": [['C_1_a', 'C_1_b', 'C_1_c', 'C_1_d', 'C_1_e', 'C_1_f']],
    "Foci": [['F_1_a', 'F_1_b', 'F_1_c', 'F_1_d', 'F_1_e', 'F_1_f']],
    "Points P": [['P_1_a', 'P_1_b', 'P_1_c']],
    "Centers": [[(3.5, 0), (1.0, 0), (6.0, 0)]],
    "Points P Coordinates": [[(3.5000000000000004, 3.122498999199199), (1.7949984102035197, 0.9929526960901777), (5.20500158979648, 0.9929526960901777)]],
    "Chord Lengths": [[[4.0, 3.9999999999999996], [1.9968740067521995, 1.0031259939978001], [1.0031259939978003, 1.9968740067522]]]
}

df = pd.DataFrame(data)

# Unnesting the data into 6 rows
unnested_data = {
    "Recursion": [],
    "Ellipses": [],
    "Chords": [],
    "Foci": [],
    "Points P": [],
    "Centers": [],
    "Points P Coordinates": [],
    "Chord Lengths": []
}

for i in range(df["Number of Chords"][0]):
    ellipse_index = i % df["Number of Ellipses"][0]
    foci_index = i % df["Number of Foci"][0]
    point_index = i % df["Number of Points P"][0]
    center_index = i % len(df["Centers"][0])
    
    unnested_data["Recursion"].append(df["Recursion"][0])
    unnested_data["Ellipses"].append(df["Ellipses"][0][ellipse_index])
    unnested_data["Chords"].append(df["Chords"][0][i])
    unnested_data["Foci"].append(df["Foci"][0][foci_index])
    unnested_data["Points P"].append(df["Points P"][0][point_index])
    unnested_data["Centers"].append(df["Centers"][0][center_index])
    unnested_data["Points P Coordinates"].append(df["Points P Coordinates"][0][center_index])
    unnested_data["Chord Lengths"].append(df["Chord Lengths"][0][ellipse_index][i % 2])

unnested_df = pd.DataFrame(unnested_data)
print(unnested_df.to_html(index=False))

Output

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th>Recursion</th>
      <th>Ellipses</th>
      <th>Chords</th>
      <th>Foci</th>
      <th>Points P</th>
      <th>Centers</th>
      <th>Points P Coordinates</th>
      <th>Chord Lengths</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>E_1_a</td>
      <td>C_1_a</td>
      <td>F_1_a</td>
      <td>P_1_a</td>
      <td>(3.5, 0)</td>
      <td>(3.5000000000000004, 3.122498999199199)</td>
      <td>4.000000</td>
    </tr>
    <tr>
      <td>1</td>
      <td>E_1_b</td>
      <td>C_1_b</td>
      <td>F_1_b</td>
      <td>P_1_b</td>
      <td>(1.0, 0)</td>
      <td>(1.7949984102035197, 0.9929526960901777)</td>
      <td>1.003126</td>
    </tr>
    <tr>
      <td>1</td>
      <td>E_1_c</td>
      <td>C_1_c</td>
      <td>F_1_c</td>
      <td>P_1_c</td>
      <td>(6.0, 0)</td>
      <td>(5.20500158979648, 0.9929526960901777)</td>
      <td>1.003126</td>
    </tr>
    <tr>
      <td>1</td>
      <td>E_1_a</td>
      <td>C_1_d</td>
      <td>F_1_d</td>
      <td>P_1_a</td>
      <td>(3.5, 0)</td>
      <td>(3.5000000000000004, 3.122498999199199)</td>
      <td>4.000000</td>
    </tr>
    <tr>
      <td>1</td>
      <td>E_1_b</td>
      <td>C_1_e</td>
      <td>F_1_e</td>
      <td>P_1_b</td>
      <td>(1.0, 0)</td>
      <td>(1.7949984102035197, 0.9929526960901777)</td>
      <td>1.996874</td>
    </tr>
    <tr>
      <td>1</td>
      <td>E_1_c</td>
      <td>C_1_f</td>
      <td>F_1_f</td>
      <td>P_1_c</td>
      <td>(6.0, 0)</td>
      <td>(5.20500158979648, 0.9929526960901777)</td>
      <td>1.996874</td>
    </tr>
  </tbody>
</table>

Data as Table

No DataFrame found in the output.

Code Execution Output

Python Code Example


import pandas as pd

# Initial data
data = {
    "Recursion": [1],
    "Number of Ellipses": [3],
    "Number of Chords": [6],
    "Number of Foci": [6],
    "Number of Points P": [3],
    "Ellipses": [['E_1_a', 'E_1_b', 'E_1_c']],
    "Chords": [['C_1_a', 'C_1_b', 'C_1_c', 'C_1_d', 'C_1_e', 'C_1_f']],
    "Foci": [['F_1_a', 'F_1_b', 'F_1_c', 'F_1_d', 'F_1_e', 'F_1_f']],
    "Points P": [['P_1_a', 'P_1_b', 'P_1_c']],
    "Centers": [[(3.5, 0), (1.0, 0), (6.0, 0)]],
    "Points P Coordinates": [[(3.5000000000000004, 3.122498999199199), (1.7949984102035197, 0.9929526960901777), (5.20500158979648, 0.9929526960901777)]],
    "Chord Lengths": [[[4.0, 3.9999999999999996], [1.9968740067521995, 1.0031259939978001], [1.0031259939978003, 1.9968740067522]]]
}

df = pd.DataFrame(data)

# Unnesting the data into 6 rows
unnested_data = {
    "Recursion": [],
    "Ellipses": [],
    "Chords": [],
    "Foci": [],
    "Points P": [],
    "Centers": [],
    "Points P Coordinates": [],
    "Chord Lengths": []
}

for i in range(df["Number of Chords"][0]):
    ellipse_index = i % df["Number of Ellipses"][0]
    foci_index = i % df["Number of Foci"][0]
    point_index = i % df["Number of Points P"][0]
    center_index = i % len(df["Centers"][0])
    
    unnested_data["Recursion"].append(df["Recursion"][0])
    unnested_data["Ellipses"].append(df["Ellipses"][0][ellipse_index])
    unnested_data["Chords"].append(df["Chords"][0][i])
    unnested_data["Foci"].append(df["Foci"][0][foci_index])
    unnested_data["Points P"].append(df["Points P"][0][point_index])
    unnested_data["Centers"].append(df["Centers"][0][center_index])
    unnested_data["Points P Coordinates"].append(df["Points P Coordinates"][0][center_index])
    unnested_data["Chord Lengths"].append(df["Chord Lengths"][0][ellipse_index][i % 2])

unnested_df = pd.DataFrame(unnested_data)
display(unnested_df)

Output