With the database set up, we can now add more models. Thinking about it, we can see three models right now: User (which we already have). Feedback. Town. model User { id Int @id @default(autoincrement()) email String ...
#UML Dictionary Learning import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import fetch_olivetti_faces from sklearn.decomposition import MiniBatchDictionaryLearning from sklearn.feature_extraction.image import extract_patches_...
#Visualisation for the t-SNE process UML #We calculate pairwise similarities in the high dimensional space using Euclidean distance #We estimate probability distributions in the high- dimensional space based on the nearest neighbors and their distan...
#Blind Source Separation (Cocktail Party Problem) using ICA #Objective: To apply ICA for separating mixed audio signals into their original independent sources #Steps to Implement ICA Generate or Load Mixed Signals: #We will use artificially mised ...
#PCA Dimensionality Reduction ## Question 1: IRIs # Import necessary libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.decomposition import PCA from sklearn.preprocess...
Structural design patterns are a category of software design patterns that focus on how objects and classes are organized to for larger structures keeping systems flexible and efficient. These patterns help define relationships between entities whil...
import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_blobs #generate synthetic data np.random.seed(42) data, _ = make_blobs(n_samples=300, centers=3, cluster_std=1.0, random_state=42) # Make shift implementation def ...
import pandas as pd from mlxtend.frequent_patterns import fpgrowth, association_rules from graphviz import Digraph class FPTreeNode: def __init__(self, name, count, parent): self.name = name self.count = count self.parent...
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.cluster import DBSCAN from sklearn.preprocessing import StandardScaler #Dataset data = np.array([ [15,2],[16,3],[15.5,2.5],[18,4],[50,40],[51,42],[49,39],[12,2],...
#####################For 1-Dimensional Data############################### import numpy as np import pandas as pd import scipy.cluster.hierarchy as sch import matplotlib.pyplot as plt from sklearn.cluster import AgglomerativeClustering # Data points...
