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<[0]: import os
[1]: import numpy as np
[2]: from scipy.spatial.distance import cdist
[3]: from sklearn.cluster import DBSCAN
[4]: from sklearn.decomposition import PCA
[5]: from .utils import save_model
[6]: class KMeans:
[7]: """
[8]: Implementation of K-Means algorithm.
[9]: Parameters
[10]: ———-
[11]: n_clusters : int
[12]: Number of clusters.
[13]: Attributes
[14]: ———-
[15]: centroids_ : ndarray of shape (n_clusters, n_features)
[16]: labels_ : ndarray of shape (n_samples,)
[17]: inertia_ : float
[18]: """
[19]: def __init__(self,
[20]: n_clusters=8,
):
self.n_clusters = n_clusters
if not isinstance(n_clusters,int):
raise TypeError("'n_clusters' must be an integer number.")
if n_clusters= 1.”)
self.centroids_ = None
self.labels_ = None
self.inertia_ = None
def fit(self,X):
X = np.asarray(X,dtype=np.float32)
if len(X.shape) != 2:
raise ValueError(“X must be a 2D array.”)
if X.shape[-1] > np.iinfo(np.int32).max:
raise ValueError(“Number of features can’t exceed ‘np.iinfo(np.int32).max’.”)
self._fit(X)
def _fit(self,X):
print(‘Fitting…’)
print(‘X.shape:’,X.shape)
print(‘Initializing centroids…’)
self.centroids_ = X[np.random.choice(X.shape[0],size=self.n_clusters)]
print(‘Centroids initialized.’)
print(‘Iterating…’)
converged = False
iteration=0
iteration+=1
distances_to_centroids = cdist(X,self.centroids_)
labels = np.argmin(distances_to_centroids,axis=-1)
new_centroids = np.array([X[i == labels].mean(axis=0) for i in range(self.n_clusters)])
delta_centr = abs(new_centroids – self.centroids_)
converged=np.all(delta_centr<0.00001)
if converged==True:
break
else:
self.centroids_=new_centroids
def predict(self,X):
X = np.asarray(X,dtype=np.float32)
def save_model(self,path):
class PCAKMeans(KMeans):
class KMeansPlusPlus(KMeans):
class SpectralClustering():
***** Tag Data *****
ID: 1
description: Initialization method for KMeans class that includes various validation
checks for input parameters.
start line: 19
end line: 51
dependencies:
– type: Class
name: KMeans
start line: 6
end line: 18
context description: This snippet initializes the KMeans class by validating input
parameters such as ensuring 'n_clusters' is an integer greater than or equal to one.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 3
advanced coding concepts: 4
interesting for students: 5
self contained: N
*************
## Suggestions for complexity
Here are five advanced modifications specific to this code:
1. **Dynamic Cluster Adjustment**: Implement logic within `fit` method that dynamically adjusts `n_clusters` based on data characteristics such as variance or distribution patterns detected during runtime.
2. **Custom Distance Metrics**: Allow users to pass custom distance metrics instead of default Euclidean distance when calculating distances between points and centroids.
3. **Parallel Processing**: Introduce parallel processing capabilities within `_fit` method to handle large datasets efficiently by distributing computation across multiple cores.
4. **Incremental Learning**: Modify `fit` method to support incremental learning where new data points can be added without retraining from scratch.
5. **Visualization Integration**: Integrate visualization capabilities directly into the class so that after fitting the model, users can visualize clusters along with centroids using libraries like Matplotlib or Seaborn.
## Conversation
: Hi I need help adding some advanced features to my kmeans implementation [SNIPPET]
: Sure! What kind of features are you looking to add?
: Can we make it adjust clusters dynamically based on data variance? Like if variance too high split cluster
: Absolutely! You’ll need to modify your `_fit` method to calculate variance during each iteration and then decide whether to split clusters based on a threshold value you define.
Here’s how you might start:
python
import numpy as np
def _fit(self, X):
# Initial setup…
max_iterations = self.max_iterations if hasattr(self,’max_iterations’) else 300
for _ in range(max_iterations):
# Assign clusters…
distances_to_centroids = cdist(X,self.centroids_,metric=’euclidean’)
labels = np.argmin(distances_to_centroids,axis=1)
# Calculate new centroids…
new_centroids = np.array([X[labels == i].mean(axis=0) for i in range(self.n_clusters)])
# Check convergence…
shift_distances = np.linalg.norm(new_centroids – self.centroids_, axis=1).max()
if shift_distances variance_threshold:
# Splitting logic here… e.g., create two new centroids around original centroid[idx]
original_centroid = self.centroids_[idx]
perturbation_vector = np.random.normal(0, scale=variance_threshold/10., size=self.centroids_.shape[-1])
new_n_clusters.append(original_centroid + perturbation_vector)
new_n_clusters.append(original_centroid – perturbation_vector)
else:
new_n_clusters.append(original_centroid)
# Update number of clusters…
self.n_clusters += len(new_n_clusters) – len(self.centroids_)
self.centroids_ = np.array(new_n_clusters)
print(f”Iteration {_}: Shift {shift_distances}, Variances {variances}”)
This code provides a basic framework; you will need to refine splitting logic according to your specific needs.
: looks good but how about custom distance metrics like Manhattan distance?
: Great idea! You can allow users to specify custom distance metrics by adding an additional parameter `metric` which defaults to ‘euclidean’. Then use this metric when calculating distances.
Here’s how you can modify it:
python
from scipy.spatial.distance import cdist
def __init__(self,
n_clusters=8,
metric=’euclidean’, # New parameter added here.
max_iterations=300,
tol=1e-4):
…
self.metric = metric # Store it as an attribute.
And update `_fit` method:
python
def _fit(self,X):
…
distances_to_centroids = cdist(X,self.centroids_,metric=self.metric)
…
Now users can initialize `KMeans` with different metrics like so:
python
kmeans_instance = KMeans(n_clusters=8, metric=’cityblock’)
This allows flexibility without breaking existing functionality.
: How about making it work incrementally? Like I want add more data later without retraining everything.
: Incremental learning is quite complex but achievable! You’ll need methods that update centroids based on incoming data batches without starting over.
Here’s a basic outline:
Firstly, introduce an `update` method:
python
def update(self,X_new):
“””Update model incrementally with new data.”””
X_new_arrayed=X_new.astype(np.float32)
distances_to_current_centroid=cudist(X_new_arrayed,self.centroid_,metric=self.metric)
labels=np.argmin(distances_to_current_centroid,axis=-1)
updated_count=[np.sum(labels==i)foriinaange(len(centr))]
total_count=[np.sum(labels==i)+old_count[i]fori,inrange(len(centr))]
updated_centr=[(X_new_arrayed[label==i].mean(axis=0)*updated_count[i]+self.centr[i]*old_count[i])/total_count[i]fori,inrange(len(centr))]
centr_updated=np.array(updated_centr)
shift_distance=np.linalg.norm(centr_updated-self.centr,axis=-1).max()
if shift_distance<=self.tol:self.centr_=centr_updated
Then call this method whenever you have new data:
python
kmeans_instance.update(new_data_batch)
This way your model adapts incrementally without full retraining!<|
***** Tag Data *****
ID: 3
description: Fitting process within _fit method which iteratively updates cluster centroids.
start line: 52
end line: 125
dependencies:
– type: Method/Function/Class Name/Other Contextual Information Relevant To Understanding The Snippet But Not Directly Part Of It.
start line: null end line: null description content relevant context information needed but not direct part.
context description: This snippet details the iterative process within _fit where cluster-centers'
are recalculated until convergence is achieved.
algorithmic depth: 5
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 5
interesting for students: 5
self contained: Y
*************
## Suggestions for complexity
Here are five advanced ways that could expand or modify the logic specific to the provided code snippet:
1. **Adaptive Convergence Threshold:** Implement an adaptive mechanism where the convergence threshold (`delta_centr`) changes dynamically based on certain conditions like iteration count or variability among cluster centers.
2. **Parallelized Centroid Calculation:** Modify `_fit` function such that centroid recalculation (`new_centroid`) happens concurrently using multi-threading or multiprocessing techniques.
3. **Outlier Detection:** Add functionality within `_fit` loop that detects outliers within each cluster using statistical methods (like z-score), then either removes them from consideration or adjusts centroid calculations accordingly.
4. **Weighted Clustering:** Incorporate weights into clustering such that each point contributes differently based on some criteria (like density), modifying both distance calculation (`cdist`) and centroid updating steps accordingly.
5. **Custom Distance Metric:** Allow user-defined distance metrics beyond standard ones provided by `cdist`, requiring changes in how distances are computed between points and centroids.
## Conversation
<userHey AI I need some help understanding this piece of code related iterative process [SNIPPET]
<assistantSure thing! The provided snippet shows part of an iterative process inside a `_fit` function likely used in clustering algorithms like k-means where cluster centers are recalculated until convergence is reached.
The main parts include initialization steps followed by iterating until convergence criteria are met—specifically checking whether changes (`delta_centr`) between old and newly calculated centroids fall below a small threshold (`0.00001`). Would you like me to go into detail about any particular section?
<userYeah so I'm thinking about making delta_centr adaptive instead fixed value right now its too rigid
