def exampleB2_5(G=40, T= 1.174, gamma = 42.58, alpha = np.arange(180,60,-1)):
#% Effect of Crushers
#G = 40; % mT/m;
#T = 1.174; % ms.
#gamma = 42.58 % kHz/mT
#alpha = [180:-1:60];
#% 2 cycles/mm.
x = np.arange(0,1,0.01)/1000; #% m.
Grot = 360*x*G*T*gamma; #% Rotation due to gradient at each voxel.
Ms=[[1],[0],[0]]; #% Mstart
Mend = []
for alpha_i in alpha:
for Grot_i in Grot:
M = Ms;
M = zrot(Grot_i)*M; #% Crusher 1
M = xrot(alpha_i)*M; #% Refocusing pulse.
M = zrot(Grot_i)*M; #% Crusher 2
Mend.append(M) # Mend(:,k)=M; I think this is what you want
#end;
#%figure(3);
#%plot(abs(Mend(1,:)+i*Mend(2,:)));
figure(1);
Mxy = Mend[0]+1j*Mend[1];
plot(x*1000,real(Mxy),'k--'); hold on;
axis([np.min(x)*1000, np.max(x)*1000, -1.2, 1.2]);
plot([np.min(x), np.max(x)]*1000,[1 1]*np.abs(mean(Mxy)),'b-');
#hold off;
#grid on;
xlabel('Position (mm)'); ylabel('Signal');
legend('M_{xy}','Avg M_{xy}');
tt = sprintf('%d Degree Refocusing Angle',alpha(n)); title(tt);
setprops();
drawnow;
#%fig2tiff('crush',n);
print(n)
Mse(n) = np.abs(np.mean(Mxy));
figure(2);
plot(alpha,Mse); #grid on;
xlabel('Refocusing Angle (deg)');
ylabel('Spin Echo Signal');
title('Spin Echo vs Refoc. Angle');
a = plt.gca();
#axis([a(1:2) 0 1]);
mrs.setprops();
import pandas as pd
# load the dataset and view top five records
dataset = pd.read_csv('Mall_Customer.csv')
x = dataset.iloc[:, [3, 4]].values
dataset.head()
# using the elbow method to find the optimal number of clusters
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)
kmeans.fit(x)
wcss.append(kmeans.inertia_)
plt.plot(range(1, 11), wcss)
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()
# train the K-Mean model using dataset
kmeans = KMeans(n_clusters=5, init='k-means++', random_state=42)
y_kmeans = kmeans.fit_predict(x)
# visualising the clusters
plt.scatter(x[y_hc == 0, 0],
x[y_hc == 0, 1],
s=100,
c='red',
label='Cluster 1')
plt.scatter(x[y_hc == 1, 0],
layers.Dense(512, activation="relu", input_shape=(None, data.shape[-1])))
model1.add(layers.Dropout(0.5))
model1.add(layers.LSTM(32, dropout=0.5, return_sequences=True))
model1.add(layers.LSTM(64, dropout=0.5))
model1.add(layers.Dense(512, activation="relu"))
model1.add(layers.Dropout(0.5))
model1add(layers.Dense(8, activation="softmax"))
model1.summary()
model1.compile(optimizer=optimizers.RMSprop(lr=0.0001),
loss="categorical_crossentropy",
metrics=["acc"])
history1 = model1.fit_generator(train_gen,
steps_per_epoch=train_steps,
epochs=200,
validation_data=val_gen,
validation_steps=val_steps)
loss = history1.history["loss"]
val_loss = history1.history["val_loss"]
acc = history1.history["acc"]
val_acc = history1.history["val_acc"]
epochs = range(1, len(loss) + 1)
plt.figure(figsize=(10, 5))
plt.plot(epochs, acc, "b", c="black", label="acc")
plt.plot(epochs, val_acc, "b", c="green", label="val_acc")
plt.title("Train and validation curve")
plt.legend()
model1.save("T1001.h5")
# load the dataset and view top five records
dataset = pd.read_csv('Mall_Customer.csv')
x = dataset.iloc[:, [3, 4]].values
# train the Hierarchical Clustering model using dataset
from sklearn.cluster import AgglomerativeClustering
hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidian', linkage = 'ward')
y_hc = hc.fit_predict(x)
# visualising the clusters
plt.scatter(x[y_hc == 0, 0], x[y_hc == 0, 1], s = 100, c = 'red', label = 'Cluster 1')
plt.scatter(x[y_hc == 1, 0], x[y_hc == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')
plt.scatter(x[y_hc == 2, 0], x[y_hc == 2, 1], s = 100, c = 'green', label = 'Cluster 3')
plt.scatter(x[y_hc == 3, 0], x[y_hc == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')
plt.scatter(x[y_hc == 4, 0], x[y_hc == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')
plt.title('Clusters of customers')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show()
# https://www.kaggle.com/shwetabh123/mall-customers
# 10 Models for Clustering
# K-Means
# Affinity Propagation
# BIRCH
# DBSCAN
# Mini Batch K-Means