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Hello World

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from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import numpy as np
from matplotlib import cm
from sklearn.metrics import silhouette_samples
import pandas as pd
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import linkage
from scipy.cluster.hierarchy import dendrogram

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import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import utils

from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

np.random.seed(0)

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import numpy as np
from matplotlib import pyplot

# Some functions to plot our points and draw the lines
def plot_points(features, labels, fix_margins=True):
    X = np.array(features)
    y = np.array(labels)
    spam = X[np.argwhere(y==1)]
    ham = X[np.argwhere(y==0)]
    if fix_margins:

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description: Change BigQuery table permission
group: cli_gcp_qb
cmd: gsutil [table_name] [service_account]

sithu

import pandas as pd
import numpy as np
from matplotlib import pyplot

# Some functions to plot our points and draw the lines
def plot_points(features, labels):
    X = np.array(features)
    y = np.array(labels)
    spam = X[np.argwhere(y==1)]
    ham = X[np.argwhere(y==0)]

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# Loading the one_circle dataset
# ploy_svm.csv: https://gist.github.com/sithu/1a3c2dfbca74540fb2ee5a1aca1c4a0f
circular_data = pd.read_csv('poly_svm.csv')
features = np.array(circular_data[['x_1', 'x_2']])
labels = np.array(circular_data['y'])
utils.plot_points(features, labels)

# TODO: Degree = 2 vs Degree = 4
# Which one gives better accuracy?
svm_degree_2 = SVC(kernel='poly', degree=2)

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# https://gist.github.com/sithu/4722649d23c83440f2067ed429fa434b
import utils
from sklearn.svm import SVC

# Loading the linear dataset
# linear.csv: https://gist.github.com/sithu/701d1182d63b01e740bb244d8059ceb1
linear_data = pd.read_csv('linear.csv')

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