import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler from sklearn.mixture import GaussianMixture from sklearn.metrics.cluster import adjusted_rand_score

irisdata = pd.read_csv("/work/Assignment5/iris.csv")

x= irisdata.iloc[:,:4] y= irisdata.iloc[:,-1]

sc =StandardScaler() sc.fit(x) std_array =sc.transform(x) X = pd.DataFrame(std_array,columns = x.columns)

cluster =GaussianMixture(n_components=3) cluster.fit(X) y_pred =cluster.predict(X) score = adjusted_rand_score(y,y_pred) score

from sklearn.decomposition import PCA pca =PCA(n_components=2) pca_array =pca.fit_transform(irisdata.drop(['species'],axis=1)) pca_df =pd.DataFrame(pca_array,columns=["PC1","PC2"]) pca_df.head()

col_code = {0:"yellow",1:"darkblue",2:"green"} label = {0:"setosa",1:"versicolor",2:"virginica"} pca_df["labels"]= pd.DataFrame(y_pred) groups = pca_df.groupby('labels') # Grouping instances based on species groups.mean()

fig, ax =plt.subplots(1,1,figsize =(15,10)) for name, group in groups: ax.plot(group.PC1,group.PC2,color =col_code[name],label =label[name],marker='o',linestyle='',ms=10) ax.legend() plt.show()