import numpy as np
import matplotlib.pyplot as plt


dpi = 120


# for equation use latex 
eqs = []
eqs.append((r"Linear Regression"))
eqs.append((r"Logistic Regression"))
eqs.append((r"Support Vector Machine"))
eqs.append((r"Decision Tree"))
eqs.append((r"K-NN"))
eqs.append((r"KMeans"))
eqs.append((r"Naive Bayes"))
eqs.append((r"Random Forest"))
eqs.append((r"AdaBoost"))
eqs.append((r"Principal Component Analysis"))
eqs.append((r"Apriori Algorithm"))


fig = plt.figure(figsize=(20, 8))

plt.axes([0.025, 0.025, 0.95, 0.95])

# w, h = fig.get_size_inches() * fig.dpi
rotations = [0, 0, 90]

for i in range(75):
    index = np.random.randint(0, len(eqs))
    eq = eqs[index]
    size = np.random.uniform(10, 28)
    x,y = np.random.uniform(0, 1, 2)
    alpha = np.random.uniform(0.35,.8)
    plt.text(x, y, eq, ha='center', va='center', color="#808080", alpha=alpha, 
             rotation=np.random.choice(rotations), transform=plt.gca().transAxes, 
             fontsize=size, clip_on=True)

plt.text(0.85, 0.5, 'Machine Learning Algorithms', color='k', fontsize=65,
            ha='right', va='center', alpha=1.0, transform=plt.gca().transAxes)
    
plt.xticks([]), plt.yticks([])
plt.axis("off")
plt.savefig('logo.png', dpi=dpi)
plt.show()