![# To store the value of centroids when it updates
C_old = np.zeros(C.shape)
# Cluster Lables(0, 1, 2)
clusters = np.zeros(len(X))
# Error func. - Distance between new centroids and old centroids
error = dist(C, C_old, None)
# Loop will run till the error becomes zero
while error != 0:
# Assigning each value to its closest cluster
for i in range(len(X)):
distances = dist(X[i], C)
cluster = np.argmin(distances)
clusters[i] = cluster
# Storing the old centroid values
C_old = deepcopy(C)
# Finding the new centroids by taking the average value
for i in range(k):
points = [X[j] for j in range(len(X)) if clusters[j] == i]
C[i] = np.mean(points, axis=0)
error = dist(C, C_old, None)](https://image.slidesharecdn.com/classificationanddifferentalgorithm-200616232105/85/Classificationand-different-algorithm-20-320.jpg)
Classificationand different algorithm
- 3. Algorithms
- 4. Dataset
- 6. #define X & Y # Split train & test #Fit Model #Confusion Matrix
- 7. Confusion Matrix Precision Recall Accuracy F1 Score 4 51 2 12 Samples Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. Recall is the ratio of correctly predicted positive observations to the all observations in actual class F1 Score is the weighted average of Precision and Recall
- 10. Confusion Matrix
- 12. Un-supervised Learning Unsupervised learning machine learning
- 15. Clustering is a very simple algorithm which clusters the data into a number of clusters. The above image from PyPR is an example of Clustering.
- 16. As the examples are unlabeled, clustering relies on unsupervised machine learning. If the examples are labeled, then clustering becomes classification.
- 17. Applications of Clustering in different fields
- 18. 1.Inertia: It is the sum of squared distances of samples to their closest cluster center.
- 19. â˘# Number of clusters â˘k = 3 â˘# X coordinates of random centroids â˘C_x = np.random.randint(0, np.max(X)-20, size=k) â˘# Y coordinates of random centroids â˘C_y = np.random.randint(0, np.max(X)-20, size=k) â˘C = np.array(list(zip(C_x, C_y)), dtype=np.float32) â˘print("Initial Centroids") â˘print(C) â˘# Plotting along with the Centroids â˘plt.scatter(f1, f2, c='#050505', s=7) â˘plt.scatter(C_x, C_y, marker='*', s=200, c='g') Code from Scratch
- 20. # To store the value of centroids when it updates C_old = np.zeros(C.shape) # Cluster Lables(0, 1, 2) clusters = np.zeros(len(X)) # Error func. - Distance between new centroids and old centroids error = dist(C, C_old, None) # Loop will run till the error becomes zero while error != 0: # Assigning each value to its closest cluster for i in range(len(X)): distances = dist(X[i], C) cluster = np.argmin(distances) clusters[i] = cluster # Storing the old centroid values C_old = deepcopy(C) # Finding the new centroids by taking the average value for i in range(k): points = [X[j] for j in range(len(X)) if clusters[j] == i] C[i] = np.mean(points, axis=0) error = dist(C, C_old, None)
- 28. Code: Linear regression # Importing the dataset # Getting the values and plotting it
- 29. Code: Linear regression # Number of clusters # Fitting the input data # Getting the cluster labels # Centroid values
- 30. Code: Linear regression #no. of clusters #plotting the clusters #plotting the centroids