# maxim671/man-woman architecture

> Architecture diagram of the GitHub repository maxim671/man-woman, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-29). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/maxim671/man-woman
- Repository: https://github.com/maxim671/man-woman

## Overview

This small repository contains two standalone Python demonstrations of binary classification from weight and height: one trains a scalar neural network and prints a numeric prediction; the other trains with batched NumPy operations and prints a gender label. Both read iris.csv and normalize its measurements. The README provides no further product documentation, so the graph follows only the supplied source.

Main entry points: [`1.py`](https://github.com/maxim671/man-woman/blob/main/1.py) (Scalar preparation), [`batch.py`](https://github.com/maxim671/man-woman/blob/main/batch.py) (Batch preparation), [`iris.csv`](https://github.com/maxim671/man-woman/blob/main/iris.csv) (Training data)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Input data"]
  node_csv["Training data<br/>[iris.csv]"]
end

subgraph group_demos["Classifier demos"]
  node_scalar_prep["Scalar preparation<br/>[1.py]"]
  node_scalar_train["Scalar training<br/>[1.py]"]
  node_scalar_predict["Scalar prediction<br/>[1.py]"]
  node_batch_prep["Batch preparation<br/>[batch.py]"]
  node_batch_train["Batch training<br/>[batch.py]"]
  node_batch_predict["Batch prediction<br/>[batch.py]"]
end

node_user(("Runner"))

node_user -->|"runs"| node_scalar_prep
node_user -->|"runs"| node_batch_prep
node_csv -->|"read by"| node_scalar_prep
node_csv -->|"read by"| node_batch_prep
node_scalar_prep -->|"normalizes for"| node_scalar_train
node_scalar_train -->|"trains before"| node_scalar_predict
node_scalar_predict -->|"prints result"| node_user
node_batch_prep -->|"normalizes for"| node_batch_train
node_batch_train -->|"trains before"| node_batch_predict
node_batch_predict -->|"prints result"| node_user

click node_csv "https://github.com/maxim671/man-woman/blob/main/iris.csv"
click node_scalar_prep "https://github.com/maxim671/man-woman/blob/main/1.py"
click node_scalar_train "https://github.com/maxim671/man-woman/blob/main/1.py"
click node_scalar_predict "https://github.com/maxim671/man-woman/blob/main/1.py"
click node_batch_prep "https://github.com/maxim671/man-woman/blob/main/batch.py"
click node_batch_train "https://github.com/maxim671/man-woman/blob/main/batch.py"
click node_batch_predict "https://github.com/maxim671/man-woman/blob/main/batch.py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_csv toneBlue
class node_scalar_prep,node_scalar_train,node_scalar_predict,node_batch_prep,node_batch_train,node_batch_predict toneAmber
class node_user toneIndigo
```

## Components

### Input data

- [Training data](https://github.com/maxim671/man-woman/blob/main/iris.csv): `iris.csv`

### Classifier demos

- [Scalar preparation](https://github.com/maxim671/man-woman/blob/main/1.py): `1.py`
- [Scalar training](https://github.com/maxim671/man-woman/blob/main/1.py): `1.py`
- [Scalar prediction](https://github.com/maxim671/man-woman/blob/main/1.py): `1.py`
- [Batch preparation](https://github.com/maxim671/man-woman/blob/main/batch.py): `batch.py`
- [Batch training](https://github.com/maxim671/man-woman/blob/main/batch.py): `batch.py`
- [Batch prediction](https://github.com/maxim671/man-woman/blob/main/batch.py): `batch.py`

### Other components

- **Runner**

## Connections

- Runner → Scalar preparation: runs
- Runner → Batch preparation: runs
- Training data → Scalar preparation: read by
- Training data → Batch preparation: read by
- Scalar preparation → Scalar training: normalizes for
- Scalar training → Scalar prediction: trains before
- Scalar prediction → Runner: prints result
- Batch preparation → Batch training: normalizes for
- Batch training → Batch prediction: trains before
- Batch prediction → Runner: prints result