changed the colors of plot_compare_cross_decompisition.py to default matplotlib
@@ -62,9 +62,9 @@ X_test_r, Y_test_r = plsca.transform(X_test, Y_test)
plt.figure(figsize=(12, 8))
plt.subplot(221)
plt.scatter(X_train_r[:, 0], Y_train_r[:, 0], label="train",
- marker="o", c="b", s=25)
+ marker="o", s=25)
plt.scatter(X_test_r[:, 0], Y_test_r[:, 0], label="test",
- marker="o", c="r", s=25)
+ marker="o", s=25)
plt.xlabel("x scores")
plt.ylabel("y scores")
plt.title('Comp. 1: X vs Y (test corr = %.2f)' %
@@ -75,9 +75,9 @@ plt.legend(loc="best")
plt.subplot(224)
plt.scatter(X_train_r[:, 1], Y_train_r[:, 1], label="train",
- marker="o", c="b", s=25)
+ marker="o", s=25)
plt.scatter(X_test_r[:, 1], Y_test_r[:, 1], label="test",
- marker="o", c="r", s=25)
+ marker="o", s=25)
plt.xlabel("x scores")
plt.ylabel("y scores")
plt.title('Comp. 2: X vs Y (test corr = %.2f)' %
@@ -89,9 +89,9 @@ plt.legend(loc="best")
# 2) Off diagonal plot components 1 vs 2 for X and Y
plt.subplot(222)
plt.scatter(X_train_r[:, 0], X_train_r[:, 1], label="train",
- marker="*", c="b", s=50)
+ marker="*", s=50)
plt.scatter(X_test_r[:, 0], X_test_r[:, 1], label="test",
- marker="*", c="r", s=50)
+ marker="*", s=50)
plt.xlabel("X comp. 1")
plt.ylabel("X comp. 2")
plt.title('X comp. 1 vs X comp. 2 (test corr = %.2f)'
@@ -102,9 +102,9 @@ plt.yticks(())
plt.subplot(223)
plt.scatter(Y_train_r[:, 0], Y_train_r[:, 1], label="train",
- marker="*", c="b", s=50)
+ marker="*", s=50)
plt.scatter(Y_test_r[:, 0], Y_test_r[:, 1], label="test",
- marker="*", c="r", s=50)
+ marker="*", s=50)
plt.xlabel("Y comp. 1")
plt.ylabel("Y comp. 2")
plt.title('Y comp. 1 vs Y comp. 2 , (test corr = %.2f)'
