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();
def plot_perm_sample(data1, data2, n=50):
perm_sample_1, perm_sample_2 = permutation_sample(data1, data2)
def ecdf(data):
"""compute ECDF for a one-dimensional array of measurements"""
n = len(data)
x = np.sort(data)
y = np.arange(1, n + 1) / n
return x, y
x_1, y_1 = ecdf(perm_sample_1)
x_2, y_2 = ecdf(perm_sample_2)
_ = plt.plot(x_1,
y_1,
marker='.',
linestyle='none',
color='red',
alpha=0.2)
_ = plt.plot(x_2,
y_2,
marker='.',
linestyle='none',
color='blue',
alpha=0.2)
x_1, y_1 = ecdf(data1)
x_2, y_2 = ecdf(data2)
_ = plt.plot(x_1, y_1, marker='.', linestyle='none', color='red')
_ = plt.plot(x_2, y_2, marker='.', linestyle='none', color='blue')
plt.margins(0.02)
_ = plt.xlabel('x')
_ = plt.ylabel('ECDF')
plt.show()
# 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],
x[y_hc == 1, 1],
#デフォルトでL2正則化alpha = 1
import mglearn
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_breast_cancer
import maplotlib.pyplot as plt
cancer = load_breast_cancer()
x_tr, x_te, y_tr, y_te = train_test_split(cancer.data,
cancer.target,
random_state=42)
logreg = LogisticRegression().fit(x_tr, y_tr)
print("score {}".format(logreg.score(x_te, y_te)))
#plt で グラフの表示、消すのを一つのコマンドラインで スレッドでも使うのかなあ？
plt.plot(logreg.coef_.T, 'o', label="c = 1")
plt.xticks(range(cancer.data.shape[1]), cancer.feature_names, rotation=90)
plt.hlines(0, 0, cancer.data.shape[1])
plt.ylim(-5, 5)
plt.xlabel("features")
plt.ylabel("coef magnitude")
plt.legend()
plt.show()
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
# Mean Shift
k_means = KMeans(init = "k-means++", n_clusters = clusterNum, n_init = 12)
k_means.fit(X)
labels = k_means.labels_
print(labels)
#Insights
df["Clus_km"] = labels
df.head(5)
#Centroid values are checked by averaging the features in each cluster
df.groupby('Clus_km').mean()
#Looking at the distribution of customers based on their age and income
area = np.pi * (X[:,1])**2
plt.scatter(X[:,0], X[:,3], s=area, c=labels.astype(np.float), alpha=0.5)
plt.xlabel('Age', fontsize = 18)
plt.ylabel('Income', fontsize = 16)
plt.show()
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(1, figsize = (8,6))
plt.clf()
ax = Axes3D(fig, rect=[0, 0, 0.95, 1], elev=48, azim=134)
plt.cla()
# plt.ylabel('Age', fontsize=18)
# plt.xlabel('Income', fontsize=16)
# plt.zlabel('Education', fontsize=16)
ax.set_xlabel('Education')
ax.set_ylabel('Age')