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import numpy as np
import matplotlib.pyplot as plt
%matplotlib inlinex_train = np.array([[1,1],[2,2.5],[3,1.2],[5.5,6.3],[6,9],[7,6]])
y_train = ['red','red','red','blue','blue','blue']print(x_train[5,0])
print(x_train[5,1])
print(x_train[:,0])
print(x_train[:,1])7.0
6.0
[ 1. 2. 3. 5.5 6. 7. ]
[ 1. 2.5 1.2 6.3 9. 6. ]
x_test = np.array([3,4])plt.figure()
plt.scatter(x_train[:,0],x_train[:,1],s=170,color=y_train[:])
plt.scatter(x_test[0],x_test[1],s=170,color='green')
plt.show()
def dist(x,y):
return np.sqrt(np.sum((x-y)**2))num = len(x_train)
distance = np.zeros(num)
for i in range(num):
distance[i] = dist(x_train[i],x_test)
print(distance)[ 3.60555128 1.80277564 2.8 3.39705755 5.83095189 4.47213595]
min_index = np.argmin(distance)
print(y_train[min_index])red
from sklearn import datasetsdigits = datasets.load_digits()
print(digits.images[0])[[ 0. 0. 5. 13. 9. 1. 0. 0.]
[ 0. 0. 13. 15. 10. 15. 5. 0.]
[ 0. 3. 15. 2. 0. 11. 8. 0.]
[ 0. 4. 12. 0. 0. 8. 8. 0.]
[ 0. 5. 8. 0. 0. 9. 8. 0.]
[ 0. 4. 11. 0. 1. 12. 7. 0.]
[ 0. 2. 14. 5. 10. 12. 0. 0.]
[ 0. 0. 6. 13. 10. 0. 0. 0.]]
plt.figure()
plt.imshow(digits.images[0],cmap = plt.cm.gray_r, interpolation = 'nearest')
plt.show()
print(digits.target[0])0
x_train = digits.data[0:10]
y_train = digits.target[0:10]x_test = digits.data[345]plt.figure()
plt.imshow(digits.images[345], cmap = plt.cm.gray_r, interpolation ='nearest')
plt.show()
num = len(x_train)
distance = np.zeros(num)
for i in range(num):
distance[i] = dist(x_train[i],x_test)
min_index = np.argmin(distance)
print(y_train[min_index])3
num = len(x_train)
distance = np.zeros(num)
for i in range(num):
distance[i] = dist(x_train[i],x_test)
min_index = np.argmin(distance)
print(y_train[min_index])3
num = len(x_train)
no_errors = 0
distance = np.zeros(num)
for j in range(1697,1797):
x_test = digits.data[j]
for i in range(num):
distance[i] = dist(x_train[i],x_test)
min_index = np.argmin(distance)
if y_train[min_index] != digits.target[j]:
no_errors += 1
print(no_errors)37
x_train = digits.data[0:1000]
y_train = digits.target[0:1000]
num = len(x_train)
no_errors = 0
distance = np.zeros(num)
for j in range(1697,1797):
x_test = digits.data[j]
for i in range(num):
distance[i] = dist(x_train[i],x_test)
min_index = np.argmin(distance)
if y_train[min_index] != digits.target[j]:
no_errors += 1
print(no_errors)3
X = np.array([[1,1],[2,2.5],[3,1.2],[5.5,6.3],[6,9],[7,6],[8,8]])plt.figure()
plt.scatter(X[:,0],X[:,1],s=170,color='black')
plt.show()
from sklearn.cluster import KMeansk=2
kmeans = KMeans(n_clusters = k)
kmeans.fit(X);
centroids = kmeans.cluster_centers_
labels = kmeans.labels_colors = ['r.','g.']
plt.figure()
for i in range(len(X)):
plt.plot(X[i,0], X[i,1], colors[labels[i]],markersize = 30)
plt.scatter(centroids[:,0],centroids[:,1],marker="x",s=300,linewidths=5)
plt.show()
k=3
kmeans = KMeans(n_clusters = k)
kmeans.fit(X);
centroids = kmeans.cluster_centers_
labels = kmeans.labels_
colors = ['r.','g.','y.']
plt.figure()
for i in range(len(X)):
plt.plot(X[i,0], X[i,1], colors[labels[i]],markersize = 30)
plt.scatter(centroids[:,0],centroids[:,1],marker="x",s=300,linewidths=5)
plt.show()
k=7
kmeans = KMeans(n_clusters = k)
kmeans.fit(X);
centroids = kmeans.cluster_centers_
labels = kmeans.labels_
colors = ['r.','g.','y.','c.','b.','k.','m.']
plt.figure()
for i in range(len(X)):
plt.plot(X[i,0], X[i,1], colors[labels[i]],markersize = 30)
plt.scatter(centroids[:,0],centroids[:,1],marker="x",s=300,linewidths=5)
plt.show()
corpus = ['I love CS50. Staff is awesome, awesome, awesome!',
'I have a dog and a cat.',
'Best of CS50? Staff. And cakes. Ok, CS50 staff.',
'My dog keeps chasing my cat. Dogs!']from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer(stop_words = 'english')
z = count_vect.fit_transform(corpus)
z.todense()matrix([[3, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 2, 0, 0, 0, 0, 1, 2],
[0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0]], dtype=int64)
vocab = count_vect.get_feature_names()
print(vocab)['awesome', 'best', 'cakes', 'cat', 'chasing', 'cs50', 'dog', 'dogs', 'keeps', 'love', 'ok', 'staff']
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words = 'english')
X = vectorizer.fit_transform(corpus)
X.todense()matrix([[ 0.89469821, 0. , 0. , 0. , 0. ,
0.23513012, 0. , 0. , 0. , 0.29823274,
0. , 0.23513012],
[ 0. , 0. , 0. , 0.70710678, 0. ,
0. , 0.70710678, 0. , 0. , 0. ,
0. , 0. ],
[ 0. , 0.35415727, 0.35415727, 0. , 0. ,
0.55844332, 0. , 0. , 0. , 0. ,
0.35415727, 0.55844332],
[ 0. , 0. , 0. , 0.38274272, 0.48546061,
0. , 0.38274272, 0.48546061, 0.48546061, 0. ,
0. , 0. ]])
k=2
from sklearn.metrics.pairwise import cosine_similarity
dist = 1 - cosine_similarity(X)
model = KMeans(n_clusters=k)
model.fit(X)KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=None, tol=0.0001, verbose=0)
print("Top terms per cluster:\n")
order_centroids = model.cluster_centers_.argsort()[:,::-1]
terms = vectorizer.get_feature_names()
for i in range(k):
print("Cluster %i:" % i, end='')
for ind in order_centroids[i,:3]:
print(' %s,' % terms[ind], end='')
print("")Top terms per cluster:
Cluster 0: awesome, staff, cs50,
Cluster 1: dog, cat, keeps,
import pandas as pd
from io import StringIO
import requests
act = requests.get('https://docs.google.com/spreadsheets/d/1udJ4nd9EKlX_awB90JCbKaStuYh6aVjh1X6j8iBUXIU/export?format=csv')
dataact = act.content.decode('utf-8')
frame = pd.read_csv(StringIO(dataact))
print(frame) Title Synopsis
0 Mad Max: Fury Road Max Rockatansky (Tom Hardy) explains in a voic...
1 The Matrix The screen is filled with green, cascading cod...
2 The King's Speech The film opens with Prince Albert, Duke of Yor...
3 No Country for Old Men The film opens with a shot of desolate, wide-o...
4 A Beautiful Mind John Nash (Russell Crowe) arrives at Princeton...
5 Inception A young man, exhausted and delirious, washes u...
6 Frozen The Walt Disney Pictures logo and the movie ti...
7 The Lion King The Lion King takes place in the Pride Lands o...
8 Aladdin The film starts with a street peddler, guiding...
9 Cinderella The film opens in a tiny kingdom, and shows us...
10 Finding Nemo Two clownfish, Marlin (Albert Brooks) and his ...
11 Toy Story A boy called Andy Davis (voice: John Morris) u...
12 Robin Hood Told with animals for it's cast, the story tel...
corpus = []
for i in range(0, frame["Synopsis"].size):
corpus.append(frame["Synopsis"][i])from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words = 'english', min_df = 0.2)
X = vectorizer.fit_transform(corpus)k = 2
from sklearn.metrics.pairwise import cosine_similarity
dist = 1 - cosine_similarity(X)
model = KMeans(n_clusters = k)
model.fit(X);no_words = 4
order_centroids = model.cluster_centers_.argsort()[:, ::-1]
terms = vectorizer.get_feature_names()
labels = model.labels_
print("Top terms per cluster:\n")
for i in range(k):
print("Cluster %d movies:" % i, end='')
for title in frame["Title"][labels == i]:
print(' %s,' % title, end='')
print() #add a whitespace
print("Cluster %d words:" % i, end='')
for ind in order_centroids[i, :no_words]:
print (' %s' % terms[ind], end=','),
print()
print()Top terms per cluster:
Cluster 0 movies: Mad Max: Fury Road, The Matrix, No Country for Old Men, A Beautiful Mind, Inception, The Lion King, Finding Nemo, Toy Story,
Cluster 0 words: says, tank, room, joe,
Cluster 1 movies: The King's Speech, Frozen, Aladdin, Cinderella, Robin Hood,
Cluster 1 words: king, prince, duke, palace,