def main(): X = get_simple_data() plt.scatter(X[:,0], X[:,1]) plt.show() costs = np.empty(10) costs[0] = None
def main():
X = get_simple_data()
costs = np.empty(10)
costs[0] = None
for k in range(1, 10):
c = plot_k_means(X, k)
costs[k] = c
plt.plot(costs)
plt.title('cost vs K')
plt.show()
def main():
X = get_simple_data()
plt.scatter(X[:,0], X[:,1])
plt.show()
costs = np.empty(10)
costs[0] = None
for k in range(1, 10):
M, R = plot_k_means(X, k, show_plots=False)
c = cost(X, R, M)
costs[k] = c
plt.plot(costs)
plt.title("Cost vs K")
plt.show()
def main():
X = get_simple_data()
plt.scatter(X[:, 0], X[:, 1])
plt.show()
costs = np.empty(10)
costs[0] = None
for k in range(1, 10):
M, R = plot_k_means(X, k, show_plots=False)
c = cost(X, R, M)
costs[k] = c
plt.plot(costs)
plt.title("Cost vs K")
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from kmeans import get_simple_data
from sklearn.preprocessing import StandardScaler
# get the data and standardize it
X = get_simple_data()
scaler = StandardScaler()
X = scaler.fit_transform(X)
# get shapes
N, D = X.shape
K = 3
# initialize parameters
W = np.random.randn(D, K)
# set hyperparameters
n_epochs = 100
learning_rate = 0.001
losses = []
# training loop
for i in range(n_epochs):
loss = 0
for j in range(N):
h = W.T.dot(X[j]) # K-length vector
k = np.argmax(h) # winning neuron
# accumulate loss
