v1.3.2

Older Versions/yoshi-seals1.3.1/.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "/home/hideyoshi/anaconda3/bin/python"
+}

yoshi-seals1.3.2/.vscode/launch.json

@@ -0,0 +1,15 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        }
+    ]
+}

yoshi-seals1.3.2/.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "/home/hideyoshi/anaconda3/bin/python"
+}

yoshi-seals1.3.2/README.md

@@ -1,6 +1,6 @@
 # Seals - Numeric Calculus
 
-This python package is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:
+This python namespace is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:
 
 * Scan *csv* files to make a numpy matrix.
 
@@ -20,17 +20,17 @@ This python package is made for applied Numeric Calculus of Linear Algebra. It i
 
 ## Syntax
 
-The module *scan* has a function for *Numpy* arrays and *Pandas* dataframes, and used the following syntax `Seals.scan.np(path)` for *Numpy* and `Seals.scan.pd(path)` for *Pandas*, where `path` is the path to your directory.
+To call the package *scan* use the syntax: `from Seals import scan`. The package also has a function for *Numpy* arrays and *Pandas* dataframes, and used the following syntax `scan.np(path)` for *Numpy* and `scan.pd(path)` for *Pandas*, where `path` is the path to your directory.
 
-The module *write* has a function for *Numpy* arrays and *Pandas* dataframes, and uses the following syntax `Seals.write.np(array,path)` for *Numpy*, where `array` is the matrix that you desire to output and `path` is the path to your directory, and `Seals.write.pd(df,path)` for *Pandas*, where `df` is the matrix that you desire to output and `path` is the path to your directory.
+To call the package *write* use the syntax: `from Seals import write`. The package also has a function for *Numpy* arrays and *Pandas* dataframes, and uses the following syntax `write.np(array,path)` for *Numpy*, where `array` is the matrix that you desire to output and `path` is the path to your directory, and `write.pd(df,path)` for *Pandas*, where `df` is the matrix that you desire to output and `path` is the path to your directory.
 
-The module *insert* has a function for *matrix* and another for *vector*, and it has the following syntax `Seals.insert.function(array)`, where `insert` is the *Python Module* and `function` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
+To call the package *insert* use the syntax: `from Seals import insert`. The package also has a function for *matrix* and another for *vector*, and it has the following syntax `insert.function(array)`, where `insert` is the *Python Module* and `function` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
 
 There is also a function that given a matrix it return all real eigen values
 
 ### Processes
 
-To call the module `process` use the syntax: `sl = Seals.process`, where `sl` is an instance and to use a function you have to append the desired function in front of the instance like: `sl.identity(array)`.
+To call the module `process` use the syntax: `from Seals import process as sl`, where `sl` is an instance and to use a function you have to append the desired function in front of the instance like: `sl.identity(array)`.
 
 * The function *identity* returns a *numpy* identity matrix of the order of the matrix passed into to it, and it has the following syntax `sl.identity(array)`, which `array` is a square matrix.
 

yoshi-seals1.3.2/Seals/eigen/eigen.py

@@ -24,9 +24,11 @@ def eigen(a):
     k = 0
     l = np.ones((a.shape[0]))
 
-    while (k < a.shape[0]):
+    at = a #variavel temporaria para A
 
-        u = np.random.rand(a.shape[0],1)
+    while (k < at.shape[0]):
+
+        u = np.random.rand(at.shape[0],1)
         u = u/max(u.min(), u.max(), key=abs)
 
         ctrl = 0
@@ -34,7 +36,7 @@ def eigen(a):
         while (ctrl != l[k]):
             
             ctrl = l[k]
-            u = a.dot(u)
+            u = at.dot(u)
             l[k] = max(u.min(), u.max(), key=abs)
             u = u/l[k]
 
@@ -44,8 +46,26 @@ def eigen(a):
         while (u[i] == 0):
             i += 1
 
-        a = a - (1/u[i])*u*a[i]
+        at = at - (1/u[i])*u*at[i]
-
         k += 1
 
-    return l
+    i = 0
+    b = np.random.rand(a.shape[0],a.shape[1])
+
+    while (i < l.shape[0]):
+
+        alpha = 0.999*l[i]
+
+        t = np.random.rand(a.shape[0],1)
+
+        b[i] = b[i]/max(b[i].min(), b[i].max(), key=abs)
+        t = l/max(l.min(), l.max(), key=abs)
+
+        while not (np.allclose(b[i],t,atol=10**(-17))):
+            t = b[i].copy()
+            b[i] = np.linalg.solve((a - alpha*np.identity(a.shape[0])),((l[i]-alpha)*t))
+            b[i] = b[i]/max(b[i].min(), b[i].max(), key=abs)
+        
+        i += 1
+
+    return l, b

yoshi-seals1.3.2/build/lib/Seals/eigen/eigen.py

@@ -24,9 +24,11 @@ def eigen(a):
     k = 0
     l = np.ones((a.shape[0]))
 
-    while (k < a.shape[0]):
+    at = a #variavel temporaria para A
 
-        u = np.random.rand(a.shape[0],1)
+    while (k < at.shape[0]):
+
+        u = np.random.rand(at.shape[0],1)
         u = u/max(u.min(), u.max(), key=abs)
 
         ctrl = 0
@@ -34,7 +36,7 @@ def eigen(a):
         while (ctrl != l[k]):
             
             ctrl = l[k]
-            u = a.dot(u)
+            u = at.dot(u)
             l[k] = max(u.min(), u.max(), key=abs)
             u = u/l[k]
 
@@ -44,8 +46,26 @@ def eigen(a):
         while (u[i] == 0):
             i += 1
 
-        a = a - (1/u[i])*u*a[i]
+        at = at - (1/u[i])*u*at[i]
-
         k += 1
 
-    return l
+    i = 0
+    b = np.random.rand(a.shape[0],a.shape[1])
+
+    while (i < l.shape[0]):
+
+        alpha = 0.999*l[i]
+
+        t = np.random.rand(a.shape[0],1)
+
+        b[i] = b[i]/max(b[i].min(), b[i].max(), key=abs)
+        t = l/max(l.min(), l.max(), key=abs)
+
+        while not (np.allclose(b[i],t,atol=10**(-17))):
+            t = b[i].copy()
+            b[i] = np.linalg.solve((a - alpha*np.identity(a.shape[0])),((l[i]-alpha)*t))
+            b[i] = b[i]/max(b[i].min(), b[i].max(), key=abs)
+        
+        i += 1
+
+    return l, b

yoshi-seals1.3.2/setup.py

@@ -5,7 +5,7 @@ with open("README.md", "r") as fh:
 
 setuptools.setup(
     name="yoshi-seals",
-    version="1.3",
+    version="1.3.2",
     author="Vitor Hideyoshi",
     author_email="vitor.h.n.batista@gmail.com",
     description="Numeric Calculus python module in the topic of Linear Algebra",

yoshi-seals1.3.2/yoshi_seals.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: yoshi-seals
-Version: 1.3
+Version: 1.3.2
 Summary: Numeric Calculus python module in the topic of Linear Algebra
 Home-page: https://github.com/HideyoshiNakazone/Seals-NumericCalculus.git
 Author: Vitor Hideyoshi
@@ -8,7 +8,7 @@ Author-email: vitor.h.n.batista@gmail.com
 License: UNKNOWN
 Description: # Seals - Numeric Calculus
         
-        This python package is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:
+        This python namespace is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:
         
         * Scan *csv* files to make a numpy matrix.
         
@@ -28,17 +28,17 @@ Description: # Seals - Numeric Calculus
         
         ## Syntax
         
-        The module *scan* has a function for *Numpy* arrays and *Pandas* dataframes, and used the following syntax `Seals.scan.np(path)` for *Numpy* and `Seals.scan.pd(path)` for *Pandas*, where `path` is the path to your directory.
+        To call the package *scan* use the syntax: `from Seals import scan`. The package also has a function for *Numpy* arrays and *Pandas* dataframes, and used the following syntax `scan.np(path)` for *Numpy* and `scan.pd(path)` for *Pandas*, where `path` is the path to your directory.
         
-        The module *write* has a function for *Numpy* arrays and *Pandas* dataframes, and uses the following syntax `Seals.write.np(array,path)` for *Numpy*, where `array` is the matrix that you desire to output and `path` is the path to your directory, and `Seals.write.pd(df,path)` for *Pandas*, where `df` is the matrix that you desire to output and `path` is the path to your directory.
+        To call the package *write* use the syntax: `from Seals import write`. The package also has a function for *Numpy* arrays and *Pandas* dataframes, and uses the following syntax `write.np(array,path)` for *Numpy*, where `array` is the matrix that you desire to output and `path` is the path to your directory, and `write.pd(df,path)` for *Pandas*, where `df` is the matrix that you desire to output and `path` is the path to your directory.
         
-        The module *insert* has a function for *matrix* and another for *vector*, and it has the following syntax `Seals.insert.function(array)`, where `insert` is the *Python Module* and `function` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
+        To call the package *insert* use the syntax: `from Seals import insert`. The package also has a function for *matrix* and another for *vector*, and it has the following syntax `insert.function(array)`, where `insert` is the *Python Module* and `function` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
         
         There is also a function that given a matrix it return all real eigen values
         
         ### Processes
         
-        To call the module `process` use the syntax: `sl = Seals.process`, where `sl` is an instance and to use a function you have to append the desired function in front of the instance like: `sl.identity(array)`.
+        To call the module `process` use the syntax: `from Seals import process as sl`, where `sl` is an instance and to use a function you have to append the desired function in front of the instance like: `sl.identity(array)`.
         
         * The function *identity* returns a *numpy* identity matrix of the order of the matrix passed into to it, and it has the following syntax `sl.identity(array)`, which `array` is a square matrix.