GeorgeSeif

from keras.applications.nasnet import Xception, preprocess_input
from keras.models import Sequential, Model
from keras.layers.core import Lambda
from keras.backend import tf as ktf

# Initialize a Xception model
Xception_model = Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)

# Any required pre-processing should be baked into the model
input_tensor = Input(shape=(None, None, 3))

GeorgeSeif

def preact_conv(inputs, k=3, filters=64):
    outputs = BatchNormalization()(inputs)
    outputs = Activation('relu')(outputs)
    outputs = Conv2D(filters, kernel_size=(k, k), padding='same', 
                     kernel_initializer="glorot_normal")(outputs)
    
    return outputs

def ResidualBlock(inputs, kernal_size=3, filters=64):
    outputs = preact_conv(inputs, k=kernal_size, n_filters=filters)

GeorgeSeif

from sklearn.preprocessing import StandardScaler
X = StandardScaler().fit_transform(X)

GeorgeSeif

import numpy as np

# Compute the mean of the data
mean_vec = np.mean(X, axis=0)

# Compute the covariance matrix
cov_mat = (X - mean_vec).T.dot((X - mean_vec)) / (X.shape[0]-1)


# OR we can do this with one line of numpy:

GeorgeSeif

# Compute the eigen values and vectors using numpy
eig_vals, eig_vecs = np.linalg.eig(cov_mat)

# Make a list of (eigenvalue, eigenvector) tuples
eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]

# Sort the (eigenvalue, eigenvector) tuples from high to low
eig_pairs.sort(key=lambda x: x[0], reverse=True)

GeorgeSeif

# Only keep a certain number of eigen vectors based on 
# the "explained variance percentage" which tells us how 
# much information (variance) can be attributed to each 
# of the principal components

exp_var_percentage = 0.97 # Threshold of 97% explained variance

tot = sum(eig_vals)
var_exp = [(i / tot)*100 for i in sorted(eig_vals, reverse=True)]
cum_var_exp = np.cumsum(var_exp)

GeorgeSeif

# Compute the projection matrix based on the top eigen vectors
num_features = X.shape[1]
proj_mat = eig_pairs[0][1].reshape(num_features,1)
for eig_vec_idx in range(1, num_vec_to_keep):
  proj_mat = np.hstack((proj_mat, eig_pairs[eig_vec_idx][1].reshape(num_features,1)))

# Project the data 
pca_data = X.dot(proj_mat)

GeorgeSeif

# Importing libs
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
 
# Create a random dataset
data = pd.DataFrame(np.random.random((10,6)), columns=["Iron Man","Captain America","Black Widow","Thor","Hulk", "Hawkeye"])

print(data)

GeorgeSeif

# Importing libs
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import skewnorm

# Create the data
speed = skewnorm.rvs(4, size=50) 
size = skewnorm.rvs(4, size=50)
 
# Create and shor the 2D Density plot

GeorgeSeif

# Import libs
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

# Get the data
df=pd.read_csv("avengers_data.csv")
print(df)