semvec/pypi-packages huggingface dataset:
smkml 0.0.9
A unified machine learning toolkit for classification, clustering, distance metrics, and model analysis—optimized for both supervised and unsupervised tasks.
Requires Python: >=3.6

# SMKML - Hybrid Machine Learning Algorithm

🚀 SMK: Supervised + Unsupervised Machine Learning Toolkit
SMK (smkml) is a unified Python library for seamlessly performing classification and clustering, with built-in support for SVM, KMeans, custom distance metrics, visualization, and limitations analysis. Ideal for learners, researchers, and rapid prototyping.



## Features
-> ✅ SVM Classification with optional Grid Search (hyperparameter tuning)

-> 🔄 KMeans Clustering with PCA visualization

-> 📐 Built-in Distance Metrics:

    => Euclidean

    => Manhattan

    => Minkowski (configurable p)

-> 📊 Visualization of clusters (2D PCA)

-> ⚠️ Insights: Understand limitations of SVM and k-NN

-> 💡 Unified API: Same interface for supervised & unsupervised learning

## Installation
```bash
pip install smkml

"Quick Start"

from smkml import SMK
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Create synthetic classification data
X, y = make_classification(n_samples=300, n_features=10, n_informative=8, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Initialize SMK with Grid Search enabled
model = SMK(enable_grid_search=True)

# Train model
model.fit(X_train, y_train)

# Evaluate
acc = model.score(X_test, y_test)
print("✅ Accuracy:", acc)

# Predict
preds = model.predict(X_test)
hyperdock-llamaindex 0.3.7
hyperdock-llamaindex
Requires Python: >=3.11

# Hyperdock for LlamaIndex Tools

## But seriously, what is a Hyperdock?

- Hyperdock is just a fancy name for a collection of tools. Which are, just python functions returning `str`!
- In this case, the collection will contain functions that invoke tools which are methods of subclass of `BaseToolSpec` from llamaindex.

## How to use it?

- To use tools from `llama-index`, install independent tool package first.

```bash
$ pip install llama-index-tools-duckduckgo # or maybe uv add llama-index-tools-duckduckgo
```

- Make a dock with the tools you want to use. For example, let's say you want to use `DuckDuckGoSearchRun` tool.

```python
from hyperdock_llamaindex import LlamaIndexToolRequest, dock as llamaindex_dock
from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec

# ...
dock = llamaindex_dock(
        LlamaIndexToolRequest(
            tool_func=DuckDuckGoSearchToolSpec.duckduckgo_full_search,
            tool_args={
                "max_results": 10,
            },
        )
    )
```

- You might want to use hyperpocket's auth feature while using the llamaindex tools. In that case, you can define `auth` property for the `LlamaIndexToolRequest`.
- The auth property must be possible to serialized as [`hyperpocket.tool.ToolAuth`](https://vessl-ai.github.io/hyperpocket/autoapi/libs/hyperpocket/hyperpocket/tool/tool/index.html#libs.hyperpocket.hyperpocket.tool.tool.ToolAuth).

- After you make your dock, call from_dock from your pocket instantiation. For example, Let's say you're using hyperpocket for llamaindex. You can do:

```python
from hyperpocket.tool import from_dock
from hyperpocket_llamaindex import PocketLlamaindex
from hyperdock_llamaindex import LlamaIndexToolRequest, dock as llamaindex_dock

dock = llamaindex_dock(
    #...
)
# ...
def agent():
    # ...
    # initialize the pocket
    pocket = PocketLlamaindex(
        tools=[
            *from_dock(dock),
        ]
    )
```